internal_tests/test-ate.R
In tagteam/riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks
### test-ate.R ---
#----------------------------------------------------------------------
## Author: Thomas Alexander Gerds
## Created: Jun 25 2024 (09:21)
## Version:
## Last-Updated: Jun 25 2024 (09:24)
## By: Thomas Alexander Gerds
## Update #: 2
#----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
#----------------------------------------------------------------------
##
### Code:
library(testthat)
library(data.table)
library(mets)
library(riskRegression)
library(survival)
## * Simulate data
set.seed(10)
n <- 250
tau <- 1:5
d <- sampleData(n, outcome = "competing.risks")
dFull <- d[event!=0] ## remove censoring
dSurv <- d[event!=2] ## remove competing risk
# {{{ no censoring
test_that("wglm - no censoring",{
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = dFull)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event)~X1 + X8,
time = tau[5], data = dFull))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
## ate
test.ate <- ate(test, data = dFull, times = tau, treatment = "X1", verbose = FALSE)
GS.ate <- logitATE(formula = Event(time,event)~X1 + X8,
time = tau[5], data = dFull, treat.model = X1~1)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,se], unname(GS.ate$se.difriskG),
tolerance = 1e-5)
})
# }}}
# {{{ right censoring (but no competing risks)
test_that("wglm - censoring",{
#### no covariate in censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = dSurv, product.limit = TRUE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event)~X1 + X8,
cens.model = ~1,
time = tau[5], data = dSurv, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
## check weights
e.KM <- as.data.table(predictCoxPL(coxph(Surv(time,event==0) ~ 1, data = dSurv),
type = c("hazard","survival")))
## ate
test.ate <- ate(test, data = dSurv, times = tau, treatment = "X1", verbose = FALSE)
GS.ate <- suppressWarnings(logitATE(formula = Event(time,event)~X1 + X8, cens.model = ~1, cens.code = 0, cause = 1,
time = tau[5], data = dSurv, treat.model = X1~1, binreg = FALSE))
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,se], unname(GS.ate$se.difriskG),
tolerance = 1e-5)
#### stratified censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1,
times = tau, data = dSurv, product.limit = FALSE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1,
time = tau[5], data = dSurv, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
#### censoring model with continuous covariate ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1+X8,
times = tau, data = dSurv, product.limit = FALSE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1+X8,
time = tau[5], data = dSurv, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
})
# }}}
# {{{ competing risks
test_that("wglm - competing risks",{
#### no covariate in censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = d, product.limit = TRUE, cause = 1)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~1,
time = tau[5], data = d, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
## ate
test.ate <- ate(test, data = d, times = tau, treatment = "X1", verbose = FALSE)
GS.ate <- suppressWarnings(logitATE(formula = Event(time,event)~X1 + X8, cens.model = ~1, cens.code = 0, cause = 1,
time = tau[5], data = d, treat.model = X1~1, binreg = FALSE))
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,se], unname(GS.ate$se.difriskG),
tolerance = 1e-5)
#### stratified censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1,
times = tau, data = d, product.limit = FALSE, cause = 1)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1,
time = tau[5], data = d, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"],
tolerance = 1e-5)
## expect_equal(summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
#### censoring model with continuous covariate ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1+X8,
times = tau, data = d, product.limit = FALSE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1+X8,
time = tau[5], data = d, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"],
tolerance = 1e-5)
## expect_equal(summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
})
# }}}
# {{{
verbose <- FALSE
calcIterm <- function(factor, iid, indexJump, iid.outsideI){
n <- length(indexJump)
if(iid.outsideI){ ## compute iid int factor
ls.I <- lapply(1:n, function(iObs){ ## iObs <- 2
iIID <- iid[,1,iObs]
iFactor <- factor[iObs,1:indexJump[iObs]]
if(indexJump[iObs]==0){
return(rep(0,n))
}else {
return(iIID*sum(iFactor))
}
})
} else { ## compute int iid * factor
ls.I <- lapply(1:n, function(iObs){ ## iObs <- 2
iIID <- iid[,1:indexJump[iObs],iObs]
iFactor <- factor[iObs,1:indexJump[iObs]]
if(indexJump[iObs]==0){
return(rep(0,n))
}else if(indexJump[iObs]==1){
return(iIID*iFactor)
}else{
return(rowSums(rowMultiply_cpp(iIID, iFactor)))
}
})
}
return(do.call(cbind,ls.I))
}
## ** Ate using list of formulae
test_that("[ate] using list of formulae",{
set.seed(10)
d <- sampleData(100)
expect_output(print(ate(list(Hist(time,event)~X1+X2,
Hist(time,event)~X1+X2),
treatment = "X1", data = d, times = 1:5, cause = 1,
verbose = FALSE)))
})
## * [ate] G-formula only
cat("[ate] G-formula only \n")
## ** data
set.seed(10)
n <- 5e1
dtS <- sampleData(n,outcome="survival")
dtS$time <- round(dtS$time,1)
dtS$X1 <- factor(rbinom(n, prob = c(0.3,0.4) , size = 2), labels = paste0("T",0:2))
## ** Compare to fix value / explicit computation
test_that("[ate] G-formula,survival - compare to fix values / explicit computation",{
seqTime <- c(1,5,10)
fit <- cph(formula = Surv(time,event)~ X1+X2,data=dtS,y=TRUE,x=TRUE)
ateFit <- ate(fit, data = dtS, treatment = "X1", contrasts = NULL,
times = seqTime, band = FALSE, verbose = FALSE)
dt.ateFit <- as.data.table(summary(ateFit, band = TRUE, p.value = FALSE, short = 2))
## point estimate
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0" & type == "meanRisk",estimate],
c(0.05842145, 0.46346908, 0.66107744),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0" & type == "meanRisk",lower],
c(0.0000000, 0.2756147, 0.4670461),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0" & type == "meanRisk",upper],
c(0.1178148, 0.6513235, 0.8551088),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk",estimate],
c(0.0006541715, 0.0037795576, 0.0040931551),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk",lower],
c(-0.04241346, -0.24364096, -0.26383983),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk",upper],
c(0.0437218, 0.2512001, 0.2720261),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk", lowerBand],
c(-0.04230655, -0.24302677, -0.26317472),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk", upperBand],
c(0.04361489, 0.25058589, 0.27136103),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk",estimate],
c(1.011197, 1.008155, 1.006192),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk",lower],
c(0.2745873, 0.4723104, 0.5997906),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk",upper],
c(1.747808, 1.543999, 1.412593),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk", lowerBand],
c(0.26976028, 0.46879896, 0.5971274),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk", upperBand],
c(1.75263463, 1.5475109, 1.41525588),
tol = 1e-6)
## standard error
ATE <- list()
ATE.iid <- list()
for(iT in c("T0","T1","T2")){ ## iT <- "T0"
## risk
newdata0 <- copy(dtS)
newdata0$X1 <- iT
fit <- cph(formula = Surv(time,event)~ X1+X2,data=dtS,y=TRUE,x=TRUE)
resPred <- predictCox(fit, newdata = newdata0, time = seqTime, iid = TRUE)
ATE[[iT]] <- colMeans(1-resPred$survival)
ATE.iid_term1 <- apply(-resPred$survival.iid,1,rowMeans)
ATE.iid_term2 <- apply(1-resPred$survival, 1, function(x){x-ATE[[iT]]})/n
ATE.iid[[iT]] <- t(ATE.iid_term1) + t(ATE.iid_term2)
ATE.se <- sqrt(apply(ATE.iid[[iT]]^2, 2, sum))
ATE.lower <- ATE[[iT]]+ qnorm(0.025) * ATE.se
ATE.upper <- ATE[[iT]] + qnorm(0.975) * ATE.se
expect_equal(ignore_attr=TRUE,pmax(0,ATE.lower), dt.ateFit[level == iT,lower])
expect_equal(ignore_attr=TRUE,pmin(1,ATE.upper), dt.ateFit[level == iT,upper])
}
## difference in risk
diffATE <- ATE[["T1"]]-ATE[["T0"]]
diffATE.iid <- ATE.iid[["T1"]]-ATE.iid[["T0"]]
diffATE.se <- sqrt(apply(diffATE.iid^2, 2, sum))
diffATE.lower <- diffATE + qnorm(0.025) * diffATE.se
diffATE.upper <- diffATE + qnorm(0.975) * diffATE.se
expect_equal(ignore_attr=TRUE,diffATE.lower, dt.ateFit[level == "T0.T1" & type == "diffRisk", lower])
expect_equal(ignore_attr=TRUE,diffATE.lower, c(-0.04241346, -0.24364096, -0.26383983), tol = 1e-6)
expect_equal(ignore_attr=TRUE,diffATE.upper, dt.ateFit[level == "T0.T1" & type == "diffRisk", upper])
expect_equal(ignore_attr=TRUE,diffATE.upper, c(0.0437218, 0.2512001, 0.2720261), tol = 1e-6)
## ratio of the risks
ratioATE <- ATE[["T1"]]/ATE[["T0"]]
ratioATE.iid <- rowScale_cpp(ATE.iid[["T1"]],ATE[["T0"]])-rowMultiply_cpp(ATE.iid[["T0"]], ATE[["T1"]]/ATE[["T0"]]^2)
expect_equal(ignore_attr=TRUE,unname(ATE.iid[["T1"]]),unname(ateFit$iid$GFORMULA$T1))
ratioATE.se <- sqrt(rowSums(ratioATE.iid^2))
ratioATE.se <- sqrt(colSums(ratioATE.iid^2))
ratioATE.lower <- ratioATE + qnorm(0.025) * ratioATE.se
ratioATE.upper <- ratioATE + qnorm(0.975) * ratioATE.se
expect_equal(ignore_attr=TRUE,ratioATE.lower, dt.ateFit[level == "T0.T1" & type == "ratioRisk", lower])
expect_equal(ignore_attr=TRUE,ratioATE.lower, c(0.2745873, 0.47231036, 0.59979056), tol = 1e-6)
expect_equal(ignore_attr=TRUE,ratioATE.upper, dt.ateFit[level == "T0.T1" & type == "ratioRisk", upper])
expect_equal(ignore_attr=TRUE,ratioATE.upper, c(1.7478076, 1.5439995, 1.41259273))
})
## ** strata argument
test_that("[ate] G-formula,survival - strata argument",{
## Cox model
e.coxph <- coxph(Surv(time, event) ~ X1, data = dtS,
x = TRUE, y = TRUE)
outATE <- ate(e.coxph, data = dtS, treatment = NULL, strata = "X1", time = 1,
product.limit = FALSE, verbose = FALSE, band = TRUE)
dt.outATE <- as.data.table(outATE)
outPred <- predictCox(e.coxph,
newdata = dtS,
times = 1,
iid = TRUE,
se = TRUE,
keep.newdata = TRUE,
type = "survival")
GS.se <- as.data.table(outPred)[,.SD[1],by = "X1"]
setkeyv(GS.se, cols = "X1")
expect_equal(ignore_attr=TRUE,1-GS.se$survival, dt.outATE[type=="meanRisk",estimate])
expect_equal(ignore_attr=TRUE,GS.se$survival.se, dt.outATE[type=="meanRisk",se])
## Stratified Cox model
e.coxph <- coxph(Surv(time, event) ~ strata(X1), data = dtS,
x = TRUE, y = TRUE)
outATE <- ate(e.coxph, data = dtS, treatment = NULL, strata = "X1", time = 3,
verbose = FALSE, band = TRUE)
dt.outATE <- as.data.table(outATE)
outPred <- predictCox(e.coxph,
newdata = dtS,
times = 3,
se = TRUE,
keep.newdata = TRUE,
type = "survival")
GS.se <- as.data.table(outPred)[,.SD[1],by = "X1"]
setkeyv(GS.se, cols = "X1")
expect_equal(ignore_attr=TRUE,1-GS.se$survival, dt.outATE[type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,GS.se$survival.se, dt.outATE[type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,GS.se$survival.se, dt.outATE[type == "meanRisk",se])
})
## * [ate] Logistic regression
cat("[ate] Logistic regression \n")
n <- 100
set.seed(10)
dtB <- sampleData(n, outcome="binary")
## dtB <- data.table(X1 = rbinom(n,size =1,prob=0.5),
## X2 = rbinom(n,size =1,prob=0.5))
## dtB$Y <- rbinom(n, size = 1, prob = expit(dtB$X1+dtB$X2))
## dtB$X1 <- as.factor(dtB$X1)
## dtB$X2 <- as.factor(dtB$X2)
test_that("[ate] logistic regression - compare to lava",{
fitY <- glm(formula = Y ~ X1 + X2, data=dtB, family = "binomial")
fitT <- glm(formula = X1 ~ X2, data=dtB, family = "binomial")
## G-formula
e0.ate <- ate(fitY, data = dtB, treatment = "X1",
se = TRUE, iid = TRUE, B = 0, verbose = FALSE)
e.ate <- ate(fitY, data = dtB, treatment = "X1",
times = 5,
se = TRUE, iid = TRUE, B = 0, verbose = FALSE)
dt.ate <- as.data.table(e.ate)
e.lava <- lava::estimate(fitY, function(p, data){
a <- p["(Intercept)"] ; b <- p["X11"] ; c <- p["X21"] ;
R.X11 <- lava::expit(a + b + c * (data[["X2"]]=="1"))
R.X10 <- lava::expit(a + c * (data[["X2"]]=="1"))
list(risk0=R.X10,risk1=R.X11,riskdiff=R.X11-R.X10)},
average=TRUE)
expect_equal(ignore_attr=TRUE,unname(e.lava$coef), dt.ate[type %in% c("meanRisk","diffRisk"),estimate],tolerance=0.001)
expect_equal(ignore_attr=TRUE,unname(e.lava$vcov[1:2,1:2]), unname(crossprod(do.call(cbind,e.ate$iid$GFORMULA))),tolerance=0.001)
expect_equal(ignore_attr=TRUE,unname(sqrt(diag(e.lava$vcov))), c(dt.ate[type %in% c("meanRisk","diffRisk"),se]),tolerance=0.001)
## AIPTW
e.ate2 <- ate(fitY,
treatment = fitT,
data = dtB,
times = 5,
se = TRUE, iid = TRUE, B = 0, verbose = FALSE
)
dt.ate2 <- as.data.table(e.ate2)
dtB$Y0 <- 0
dtB$Y1 <- 1
## iid ate
iPredT <- predict(fitT, type = "response")
dtBC <- rbind(cbind(dtB[,.(Y,X2)], X1 = factor(0, levels = levels(dtB$X1)), X1test = dtB$X1=="0", pi = 1-iPredT),
cbind(dtB[,.(Y,X2)], X1 = factor(1, levels = levels(dtB$X1)), X1test = dtB$X1=="1", pi = iPredT))
dtBC$r <- predict(fitY, newdata = dtBC, type = "response")
dtBC[, ate := Y*X1test/pi + r*(1-X1test/pi)]
dtBC[, ate.iid := (ate-mean(ate))/.N, by = "X1"]
## iid outcome
iid.risk <- attr(predictRisk(fitY, newdata = dtBC, iid = TRUE),"iid")
nuisanceY.iid <- rowMultiply_cpp(iid.risk, scale = (1-dtBC$X1test/dtBC$pi))
dtBC$AnuisanceY.iid <- c(rowMeans(nuisanceY.iid[,dtBC$X1=="0"]),rowMeans(nuisanceY.iid[,dtBC$X1=="1"]))
## iid treatment
iid.pi <- attr(predictRisk(fitT, newdata = dtBC, iid = TRUE),"iid")
nuisanceT.iid <- rowMultiply_cpp(iid.pi, scale = (-1)^(dtBC$X1=="1")*dtBC$X1test*(dtBC$Y-dtBC$r)/dtBC$pi^2)
dtBC$AnuisanceT.iid <- c(rowMeans(nuisanceT.iid[,dtBC$X1=="0"]),rowMeans(nuisanceT.iid[,dtBC$X1=="1"]))
## global
expect_equal(ignore_attr=TRUE,dtBC[, mean(ate), by = "X1"][[2]],
dt.ate2[type=="meanRisk",estimate])
expect_equal(ignore_attr=TRUE,dtBC[, sqrt(sum( (ate.iid+AnuisanceY.iid+AnuisanceT.iid)^2 )), by = "X1"][[2]],
dt.ate2[type=="meanRisk",se])
## butils:::object2script(dt.ate2)
## GS <- data.table("type" = c("ate", "ate", "diffAte", "ratioAte"),
## "level" = c("0", "1", "0.1", "0.1"),
## "time" = c(5, 5, 5, 5),
## "value" = c(0.4167808, 0.7341667, 0.3173858, 1.7615174),
## "se" = c(0.05227194, 0.13439533, 0.14429419, 0.39122437),
## "lower" = c(0.31432969, 0.47075666, 0.03457442, 0.99473171),
## "upper" = c(0.5192320, 0.9975767, 0.6001973, 2.5283031),
## "estimator" = c("AIPTW", "AIPTW", "AIPTW", "AIPTW"))
})
## * [ate] Survival case
cat("[ate] Survival case \n")
## ** no censoring - manual computation
## *** Data
n <- 5e1
set.seed(10)
dtS <- sampleData(n,outcome="survival")
tau <- median(dtS$time)
dtS$event <- 1
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
## *** check
test_that("[ate] no censoring, survival - check vs. manual calculations", {
e.S <- coxph(Surv(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS,x = TRUE)
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit")) ## dtS$X1
## automatic calculation
e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, short = 2))
e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1[,.SD,.SDcols = names(dt.ate2)], dt.ate2, tol = 1e-8)
## manual calculation
dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.Surv <- predictCox(e.S, newdata = iDT, times = tau, type = "survival", iid = TRUE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(1-iPred.Surv$survival)]
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(-iPred.Surv$survival.iid[,1,])]
iDT[, iid.IPTW := (X1test*Y/pi - mean(X1test*Y/pi))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y/pi^2))]
iDT[, iid.AIPTW := (X1test*Y/pi + r*(1-X1test/pi) - mean(X1test*Y/pi + r*(1-X1test/pi)))/.N]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*(Y-r)/pi^2))]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(-iPred.Surv$survival.iid[,1,], scale = 1-X1test/pi))]
return(iDT)
}))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.43356267, 0.78109942),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y/pi),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.44100313, 0.74666667),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*(event==1)*(time<=tau)/pi + r*(1-X1test/pi)),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.44729274, 0.76956544),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
unname(dtCf$iid.Gformula))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
unname(dtCf$iid.IPTW))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
unname(dtCf$iid.AIPTW))
## check standard error
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.Gformula^2),by="X1f"][[2]]),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07227568, 0.09615608),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.IPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07837068, 0.12110765),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.AIPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07701387, 0.10447564),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.3475368, 0.3056635, 0.3222727),
"se" = c(0.1028439, 0.1420700, 0.1109526),
"lower" = c(0.14596632, 0.02721154, 0.10480955),
"upper" = c(0.5491072, 0.5841155, 0.5397359),
"p.value" = c(0.00072680, 0.03143674, 0.00367726),
"quantileBand" = c(1.962359, 1.973331, 1.915003),
"adj.p.value" = c(0.0007, 0.0296, 0.0056),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(1.801584, 1.693110, 1.720496),
"se" = c(0.3194896, 0.4006286, 0.3214094),
"lower" = c(1.175396, 0.907892, 1.090545),
"upper" = c(2.427772, 2.478327, 2.350447),
"p.value" = c(0.01210904, 0.08362040, 0.02498223),
"quantileBand" = c(1.971454, 1.969537, 1.952972),
"adj.p.value" = c(0.0118, 0.0832, 0.0246),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
GS1 <- dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))]
expect_equal(ignore_attr=TRUE,GS, GS1, tol = 1e-6)
## compare to ipw package
if (requireNamespace("ipw",quietly=TRUE)){
ww <- ipw::ipwpoint(exposure=X1,family="binomial",link="logit",
denominator=~X2,
data=dtS)$ipw.weights
expect_equal(ignore_attr=TRUE,ww,
dtCf[,1/pi[X1test==1], by = "time"][[2]])
}
})
## ** Censoring - manual computation
n <- 5e1
set.seed(10)
dtS <- sampleData(n,outcome="survival")
tau <- median(dtS$time)
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
dtS$C <- (dtS$time<=tau)*(dtS$event!=0)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
test_that("[ate] Censoring, survival - check vs. manual calculations", {
e.S <- coxph(Surv(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, x = TRUE , y = TRUE)
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit"))
e.C <- coxph(Surv(time, event == 0) ~ X1f + X2, data = dtS, x = TRUE, y = TRUE)
## automatic calculation
e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, p.value = TRUE, short = 2))
e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, p.value = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1[,.SD,.SDcols = names(dt.ate2)], dt.ate2, tol = 1e-8)
## ## manual calculation ## ##
iPred.Cens <- predictCox(e.C, newdata = dtS, times = pmin(tau,dtS$time-1e-10), type = "survival", iid = TRUE)
iPred.Cens$survivalDiag <- diag(iPred.Cens$survival)
iPred.Cens$survivalDiag.iid <- sapply(1:n, function(iObs){iPred.Cens$survival.iid[,iObs,iObs]})
## augmentation term
jumpC <- sort(e.C$y[e.C$y[,"status"]==1,"time"])
indexJump <- prodlim::sindex(jump.time = jumpC, eval.times = pmin(tau,dtS$time))
pred.Surv_tau <- predictCox(e.S, newdata = dtS, times = tau, type = "survival", iid = TRUE)
pred.Surv_jump <- predictCox(e.S, newdata = dtS, times = jumpC, type = "survival", iid = TRUE)
pred.Surv_beforejump <- predictCox(e.S, newdata = dtS, times = jumpC-1e-6, type = "survival", iid = TRUE)
pred.G_jump <- predictCox(e.C, newdata = dtS, times = jumpC-1e-10, type = "survival", iid = TRUE)
pred.dN_jump <- do.call(cbind,lapply(jumpC, function(iJ){iJ == dtS$time}))
pred.dLambda_jump <- predictCox(e.C, newdata = dtS, times = jumpC, type = "hazard", iid = TRUE)
pred.dM_jump <- pred.dN_jump-pred.dLambda_jump$hazard
term.SS <- colCenter_cpp(pred.Surv_jump$survival,pred.Surv_tau$survival)/pred.Surv_beforejump$survival
integrand_jump <- term.SS*pred.dM_jump/pred.G_jump$survival
augTerm <- sapply(1:n, function(iObs){
if(indexJump[iObs]==0){
return(0)
}else{
return(sum(integrand_jump[iObs,1:indexJump[iObs]]))
}
})
augTermY1.iid <- calcIterm(factor = pred.dM_jump/(pred.Surv_beforejump$survival*pred.G_jump$survival),
iid = -pred.Surv_tau$survival.iid,
indexJump = indexJump,
iid.outsideI = TRUE)
augTermY2.iid <- calcIterm(factor = -pred.dM_jump/(pred.Surv_beforejump$survival*pred.G_jump$survival),
iid = -pred.Surv_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermSurv.iid <- calcIterm(factor = - term.SS * pred.dM_jump/(pred.Surv_beforejump$survival * pred.G_jump$survival),
iid = pred.Surv_beforejump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG1.iid <- calcIterm(factor = - term.SS * pred.dM_jump/pred.G_jump$survival^2,
iid = pred.G_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG2.iid <- calcIterm(factor = - term.SS / pred.G_jump$survival,
iid = pred.dLambda_jump$hazard.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,C,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.Surv_tau <- predictCox(e.S, newdata = iDT, times = tau, type = "survival", iid = TRUE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(1-iPred.Surv_tau$survival)]
iDT[, G := as.double(iPred.Cens$survivalDiag)]
iDT[, I := augTerm]
## Gformula ##
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(-iPred.Surv_tau$survival.iid[,1,])]
## IPTW
iDT[, iid.IPTW := (X1test*Y*C/(pi*G) - mean(X1test*Y*C/(pi*G)))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y*C/(pi^2*G)))]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
## AIPTW,AIPCW ##
iDT[, iid.AIPTW.ate := (X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi - mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi))/.N]
## outcome
iDT[, iid.AIPTW.outcome := rowMeans(rowMultiply_cpp(-iPred.Surv_tau$survival.iid[,1,], (1-X1test/pi)))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY1.iid,X1test/pi))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY2.iid,X1test/pi))]
## treatment
iDT[, iid.AIPTW.treatment := rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -(X1test/pi^2)*(Y*C/G - r + I)))]
## survival
iDT[, iid.AIPTW.survival := rowMeans(rowMultiply_cpp(augTermSurv.iid, X1test/pi))]
## censoring
iDT[, iid.AIPTW.censoring := rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG1.iid,X1test/pi))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG2.iid,X1test/pi))]
## total
iDT[, iid.AIPTW := iid.AIPTW.ate + iid.AIPTW.outcome + iid.AIPTW.treatment + iid.AIPTW.survival + iid.AIPTW.censoring]
return(iDT)
}))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.38160373, 0.69088127),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G)),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.37314136, 0.75714469),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.37667566, 0.77294549),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
dtCf$iid.Gformula)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
dtCf$iid.IPTW)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
dtCf$iid.AIPTW)
## check standard errors
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.Gformula^2)), by = "X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.069586, 0.1201558),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.IPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07693792, 0.12423590),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.AIPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07484163, 0.10880191),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.3092775, 0.3840033, 0.3962698),
"se" = c(0.1227820, 0.1425698, 0.1121352),
"lower" = c(0.0686292, 0.1045717, 0.1764890),
"upper" = c(0.5499259, 0.6634350, 0.6160507),
"p.value" = c(0.01177169, 0.00707186, 0.00040954),
"quantileBand" = c(1.965073, 1.948196, 1.931335),
"adj.p.value" = c(0.0122, 0.0079, 0.0007),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(1.810468, 2.029110, 2.052019),
"se" = c(0.3948003, 0.5204967, 0.4234462),
"lower" = c(1.036673, 1.008955, 1.222079),
"upper" = c(2.584262, 3.049264, 2.881958),
"p.value" = c(0.04008663, 0.04802263, 0.01297622),
"quantileBand" = c(1.961192, 1.959446, 1.952883),
"adj.p.value" = c(0.0392, 0.0459, 0.0129),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS,dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
})
## * [ate] IPCW logistic regression
## ** no censoring
set.seed(10)
n <- 500
tau <- 1:5
d <- sampleData(n, outcome = "competing.risks")
d$Y <- (d$event == 1)*(d$time <= tau[3])
d0 <- d[event!=0] ## remove censoring
d0.0 <- copy(d0)[,X1 := factor(0,levels = levels(X1))]
d0.1 <- copy(d0)[,X1 := factor(1,levels = levels(X1))]
e.T <- glm(X1~X2+X6,family="binomial",data=d0)
myW.T0 <- (d0$X1=="0")/(1-predict(e.T, type = "response"))
myW.T1 <- (d0$X1=="1")/predict(e.T, type = "response")
test_that("[ate] IPCW LR - no censoring", {
e0.wglm <- wglm(regressor.event = ~ X1+X2+X6, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = d0)
e0.glm <- glm(Y ~ X1+X2+X6, family = binomial, data = d0)
## NOTE difference between wglm and glm is that lava:::information.glm uses numerical derivatives
## while information.wglm uses explicit formula
## this lead to small difference in the influence function and therefore in the standard error
eATE.wglm <- ate(e0.wglm, data = d0, treatment = "X1", times = tau, band = FALSE, verbose = FALSE)
eATE.glm <- ate(e0.glm, data = d0, treatment = "X1", times = tau[3], verbose = FALSE)
## e0.mets <- logitATE(Y~X1+X2+X6, treat.model=X1~X2+X6, data = d)
## eATE.boot <- ate(e0.glm, data = d0, treatment = "X1", times = tau[3], verbose = FALSE, B = 1000)
## confint(eATE.boot)$meanRisk$se ## 0.02535254 0.06612763
## confint(eATE.boot)$diffRisk$se ## 0.06984537
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$estimate, eATE.wglm$meanRisk[time==tau[3],estimate], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$se, eATE.wglm$meanRisk[time==tau[3],se], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$lower, eATE.wglm$meanRisk[time==tau[3],lower], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$upper, eATE.wglm$meanRisk[time==tau[3],upper], tol = 1e-4)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e0.glm, newdata = d0.0)), eATE.glm$meanRisk$estimate[1], tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e0.glm, newdata = d0.1)), eATE.glm$meanRisk$estimate[2], tol = 1e-5)
## eATE.mets <- logitATE(Event(time,event)~X1+X2+X6, data = d, cause = 1, time = tau[3], treat.model = X1~1)
## coef(eATE.mets)
## coef(e0.glm)
## not the same!!
## coef(e0.wglm)
eATE2.wglm <- ate(e0.wglm, data = d0, treatment = X1~X2+X6, times = tau, band = FALSE, verbose = FALSE)
eATE2.glm <- ate(e0.glm, data = d0, treatment = X1~X2+X6, times = tau[3], verbose = FALSE)
## e.mets <- logitATE(Y~X1+X2+X6, treat.model=X1~X2, data = d)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$estimate, eATE2.wglm$meanRisk[time==tau[3],estimate], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$se, eATE2.wglm$meanRisk[time==tau[3],se], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$lower, eATE2.wglm$meanRisk[time==tau[3],lower], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$upper, eATE2.wglm$meanRisk[time==tau[3],upper], tol = 1e-4)
## A (Y-f_a) / pi + f_a = AY/pi - (A/pi-1) f_a
expect_equal(ignore_attr=TRUE,mean( myW.T0 * d0$Y - (myW.T0-1) * predictRisk(e0.glm, newdata = d0.0)), eATE2.glm$meanRisk$estimate[1], tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean( myW.T1 * d0$Y - (myW.T1-1) * predictRisk(e0.glm, newdata = d0.1)), eATE2.glm$meanRisk$estimate[2], tol = 1e-5)
})
## ** censoring
## *** data
d <- data.frame("Ytrue" = c(0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
"status" = c(2, 2, 0, 1, 1, 0, 1, 0, 0, 2, 2, 0, 0, 1, 0, 1, 2, 0, 2, 1, 2, 1, 2, 0, 1, 1, 0, 1, 2, 0, 1, 2, 1, 0, 0, 1, 2, 1, 1, 0, 1, 2, 2, 1, 0, 1, 0, 0, 2, 0, 1, 1, 0, 0, 1, 0, 1, 1, 2, 0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 0, 1, 1, 1, 1, 2, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 1, 1, 1, 1, 2, 1, 1, 1, 0, 2, 2, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 2, 0, 0, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 0, 1, 1, 2, 0, 2, 2, 1, 0, 1, 2, 0, 1, 2, 0, 2, 2, 0, 2, 0, 1, 0, 1, 0, 1, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1, 1, 0, 2, 2, 1, 1, 1, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 2, 2, 1, 2, 0, 1, 2, 1, 1, 2, 0, 0, 2, 1, 2, 0, 0, 2, 2, 2, 1, 1, 0, 0, 0, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1),
"time" = c(0.1029, 0.133, 0.1403, 0.1659, 0.171, 0.1868, 0.1973, 0.2055, 0.2083, 0.2182, 0.2598, 0.3054, 0.3277, 0.3295, 0.3558, 0.3581, 0.3697, 0.3952, 0.4796, 0.4884, 0.5139, 0.6098, 0.619, 0.6255, 0.6374, 0.6517, 0.6687, 0.6856, 0.7404, 0.7557, 0.7625, 0.7757, 0.831, 0.8844, 0.9163, 0.9347, 0.9645, 1.0534, 1.078, 1.0848, 1.1464, 1.1603, 1.1778, 1.179, 1.1942, 1.1948, 1.2169, 1.2453, 1.2701, 1.2899, 1.3211, 1.3882, 1.413, 1.4392, 1.4407, 1.4407, 1.441, 1.451, 1.5042, 1.5496, 1.5595, 1.5974, 1.8156, 1.8209, 1.8228, 1.8495, 1.8504, 1.8515, 1.8887, 1.9146, 1.915, 2.0289, 2.0512, 2.0874, 2.1309, 2.1318, 2.1405, 2.1658, 2.1674, 2.1797, 2.1944, 2.2099, 2.2665, 2.2956, 2.4062, 2.444, 2.4472, 2.4835, 2.5011, 2.5401, 2.5416, 2.5741, 2.5825, 2.6255, 2.6335, 2.6452, 2.649, 2.6695, 2.7385, 2.7989, 2.8758, 2.8894, 2.9074, 2.9453, 2.996, 3.0597, 3.0712, 3.0835, 3.0847, 3.1286, 3.1626, 3.163, 3.1785, 3.182, 3.2286, 3.3285, 3.3329, 3.4164, 3.4316, 3.5536, 3.5564, 3.5627, 3.6225, 3.6551, 3.668, 3.7024, 3.7223, 3.8078, 3.808, 3.8125, 3.8339, 3.8605, 3.8658, 3.8713, 3.8919, 3.9118, 3.9612, 3.9998, 4.0246, 4.0548, 4.0558, 4.1904, 4.2288, 4.3213, 4.3842, 4.4425, 4.5236, 4.5913, 4.6208, 4.6848, 4.6911, 4.6916, 4.7735, 4.7874, 4.794, 4.8415, 4.8639, 4.871, 4.9216, 4.9305, 4.932, 4.9491, 5.0137, 5.0658, 5.066, 5.0807, 5.271, 5.3019, 5.3891, 5.3942, 5.4995, 5.7012, 5.7433, 5.7482, 5.7803, 5.7886, 5.8666, 5.868, 6.025, 6.1046, 6.1366, 6.2138, 6.2556, 6.2578, 6.2948, 6.3285, 6.4076, 6.4339, 6.6006, 6.6189, 6.6641, 6.7867, 6.822, 6.8455, 7.0006, 7.1658, 7.2079, 7.208, 7.2602, 7.2829, 7.5101, 7.5113, 7.57, 7.6057, 7.6087, 7.7833, 7.9494, 7.9787, 8.0451, 8.0859, 8.1161, 8.1361, 8.1395, 8.3796, 8.4503, 8.5977, 8.6304, 8.6412, 8.771, 8.9343, 8.9413, 8.979, 9.0034, 9.0179, 9.1283, 9.2646, 9.2819, 9.46, 9.6075, 10.0297, 10.4142, 10.4334, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11),
"A" = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1),
"X" = c(0.591, -0.7045, 0.1998, -1.2, -0.2748, 0.6488, -0.0464, 0.0709, -1.2, -0.1084, 0.1463, 1.2, 0.7424, 1.2, 1.1246, -0.2878, 0.6152, -0.42, 1.2, 1.2, 0.3618, 1.1865, 0.0818, -1.2, 0.8593, -0.1021, 1.1739, 0.2807, -0.8726, 1.0116, 0.4228, 0.6142, -0.3044, -0.7147, -0.7772, -0.5083, -0.3825, 0.7667, 1.2, 0.5253, 1.2, 1.2, -0.4935, -1.2, 0.7212, -0.9438, 0.2387, -1.2, 0.5291, -0.1674, 1.1412, -0.9328, 0.6634, 0.3333, -0.7123, 1.2, -0.4832, 0.3565, 0.5722, -1.0329, -0.2241, -0.8411, 1.2, 0.7209, 0.6254, 0.1107, -0.2716, 0.0051, -1.0889, 1.2, 1.2, -0.423, 1.2, 0.4794, -0.4668, 0.2772, -0.5024, -0.3908, -0.1373, 0.6094, 0.1762, 0.8822, 0.1645, -0.6743, -0.5285, -0.3291, 1.0172, 0.0233, -0.4747, 1.2, -1.2, 0.0924, -1.2, 0.3483, -0.386, 0.5705, 1.2, 1.1889, -0.1459, 1.1521, 1.2, 0.2255, 0.3866, 0.1753, -1.2, 1.2, -0.6759, 0.6161, -0.0756, -0.1158, -0.0654, 1.2, -0.0816, -0.8717, 0.3017, -0.982, -0.4491, 0.1316, 1.05, -0.9845, 0.3785, -0.8026, 0.2651, -0.9233, 1.2, -0.1918, 1.2, -0.7468, 0.7581, -0.3449, -1.2, -0.079, 0.0295, -0.4644, 0.1143, 0.8306, 0.0565, 1.2, -0.5819, 1.2, 0.572, -0.5393, -0.1491, 0.4831, 0.0408, -0.9452, 0.6162, 0.8923, 1.2, -0.0585, 0.0334, -0.6505, -1.0751, 0.6102, 0.717, -0.6497, -1.2, -0.3648, 0.4697, -1.0421, 0.1834, -1.2, -0.7025, -0.1347, -0.785, -0.8318, -0.7814, -0.2372, -0.2614, -1.0397, -1.2, 0.6774, -0.8028, -1.0292, -1.2, 0.0708, -0.0817, -0.4429, 0.413, -0.1401, -0.8748, -1.2, -0.7557, -1.0771, -0.838, -0.446, -0.8366, -0.526, -0.5437, -1.2, -0.6688, 0.4963, -1.2, -0.495, 0.779, -1.2, -0.7388, -0.6316, -0.6253, -0.564, 0.5762, 1.1168, 0.0583, 0.2203, -0.1059, 0.6471, -0.0811, 0.6312, 0.0156, -0.8045, -1.1241, 0.4943, -0.2819, -0.1622, 0.1603, -1.2, 0.5105, 0.4579, -0.7416, 0.8165, -1.193, -0.5748, -0.424, -1.0011, -1.2, -1.0157, 0.688, -0.8993, -0.2307, -0.0179, -0.1043, -0.8387, 0.2837, 0.1785, -0.6486, -1.2, -0.399, -0.8616, -1.2, -0.4967, -1.2, -0.7369, -5e-04, 0.1076, -1.0822, -0.2976, 0.617, -1.2, -0.428, -1.2))
d$A2 <- as.factor(d$A)
d$X2 <- d$X^2
d$Y1 <- as.numeric(d$time<=5)*as.numeric(d$status==1)
## *** prepare
## create weights for AIPTW
e.T <- glm(A2~X,family="binomial",data=d)
myW.T1 <- (d$A2=="1")/predict(e.T, type = "response")
myW.T0 <- (d$A2=="0")/(1-predict(e.T, type = "response"))
## range(1/d$PS2 - myW.T)
## range((iW.IPTW[,2]-iW.IPTW[,1]) - myW.T)
e.C <- coxph(Surv(time,status==0)~1,data=d, x = TRUE)
myW.C <- (1-(d$time < 5)*(d$status==0))/predictCoxPL(e.C, newdata = d, times = pmin(d$time,5), diag = TRUE)$survival
## range(iW.IPCW - myW.C)
## range(d[["IPCW"]] - myW.C)
## create counterfactual data
d1 <- copy(d)
d1$A <- 1
d1$A2 <- factor(1,levels = 0:1)
d0 <- copy(d)
d0$A <- 0
d0$A2 <- factor(0,levels = 0:1)
## *** test
test_that("[ate] IPCW LR - censoring (based on Paul's script and results)", {
## no censoring: G-formula on glm
e.glm <- glm(Ytrue~X+A2,family="binomial",data=d)
eATE.wglm <- ate(e.glm, data = d, treatment = "A2", band = FALSE, verbose = FALSE)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e.glm, newdata = d0)),
0.2468305, tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e.glm, newdata = d1)),
0.4593332, tol = 1e-5)
expect_equal(ignore_attr=TRUE,eATE.wglm$meanRisk$estimate,
c(0.2468305,0.4593332), tol = 1e-5)
## censoring:
## IPCW model
suppressWarnings(eW.glm <- glm(Y1~X2+A2,family="binomial",data=d, weights = myW.C))
e.wglm <- wglm(regressor.event = ~ X2+A2,
formula.censor = Surv(time,status==0) ~ 1,
times = 5, data = d, product.limit = TRUE)
## e.mets <- logitIPCW(Event(time,status)~X2+A2, data = d, cause = 1, time = 5, cens.model=~1)
## expect_equal(ignore_attr=TRUE,as.double(coef(e.mets)), as.double(coef(e.wglm)), tol = 1e-3)
## G-formula on IPCW glm
eATE.wglm <- ate(e.wglm, data = d, treatment = "A2", times = 5, band = FALSE, verbose = FALSE)
expect_equal(ignore_attr=TRUE,mean(predictRisk(eW.glm, newdata = d0)), 0.1886021, tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean(predictRisk(eW.glm, newdata = d1)), 0.4925792, tol = 1e-5)
## minor difference due to coxph using Newton Raphson instead of explicit formulae
expect_equal(ignore_attr=TRUE,eATE.wglm$meanRisk$estimate,
c(0.1886021,0.4925792), tol = 1e-3)
## minor difference due to difference weights computations
## eATE.mets <- logitIPCWATE(Event(time,status)~X2+A2, data = d, cause = 1, time = 5, treat.model = A2~1, cens.model=~1)
## expect_equal(ignore_attr=TRUE,as.double(coef(eATE.mets)), as.double(coef(e.wglm)), tol = 1e-3)
## expect_equal(ignore_attr=TRUE,summary(eATE.mets)$ateG[2:1,"Estimate"],unlist(confint(eATE.wglm)$meanRisk[,"estimate"]), tol = 1e-4)
## expect_equal(ignore_attr=TRUE,summary(eATE.mets)$ateG[2:1,"Std.Err"],unlist(confint(eATE.wglm)$meanRisk[,"se"]), tol = 1e-2)
## some difference in standard error
## AIPTW on IPCW glm
eATE2.wglm <- ate(e.wglm, treatment = A2 ~ X, censor = Surv(time,status==0) ~ 1,
product.limit = TRUE, estimator = c("Gformula","AIPTW"),
data = d, times = 5, band = FALSE, verbose = FALSE)
## eATE2.wglm$diffRisk
## estimator time A estimate.A B estimate.B estimate se lower
## 1: GFORMULA 5 0 0.1886021 1 0.4925629 0.3039607 0.06488501 0.17678846
## 2: AIPTW 5 0 0.2460310 1 0.4591394 0.2131084 0.07828391 0.05967479
## upper p.value
## 1: 0.4311330 2.804980e-06
## 2: 0.3665421 6.483893e-03
expect_equal(ignore_attr=TRUE,eATE2.wglm$meanRisk[estimator == "AIPTW",estimate],
c(mean( myW.T0*myW.C*d$Y1 - (myW.T0 - 1) * predict(eW.glm, newdata = d0, type = "response") ),
mean( myW.T1*myW.C*d$Y1 - (myW.T1 - 1) * predict(eW.glm, newdata = d1, type = "response") )),
tol = 1e-4)
## minor difference due to difference weights computations
## eATE2.mets <- logitIPCWATE(Event(time,status)~X2+A2, data = d, cause = 1, time = 5, treat.model = A2~X, cens.model=~1)
## expect_equal(ignore_attr=TRUE,as.double(coef(eATE2.mets)), as.double(coef(e.wglm)), tol = 1e-3)
## expect_equal(ignore_attr=TRUE,summary(eATE2.mets)$ateG[2:1,"Estimate"],unlist(confint(eATE2.wglm)$meanRisk[4:3,"estimate"]), tol = 1e-4)
## expect_equal(ignore_attr=TRUE,summary(eATE.mets)$ateG[2:1,"Std.Err"],unlist(confint(eATE.wglm)$meanRisk[,"se"]), tol = 1e-2)
## big difference in estimate and se. Not sure why.
})
## * [ate] Competing risk case
cat("[ate] Competing risk case \n")
## ** no censoring - manual computation
## *** Data
n <- 1e2
tau <- 1.5
set.seed(10)
dtS <- sampleData(n, outcome="competing.risks")[event != 0]
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
## *** check
test_that("[ate] no censoring, competing risks - check vs. manual calculations", {
e.S <- CSC(Hist(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, surv.type = "survival")
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit")) ## dtS$X1
## automatic calculation
suppressWarnings(e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE, cause = 1,
band = FALSE, product.limit = FALSE
))
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, p.value = TRUE, short = 2))
suppressWarnings(e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE, cause = 1,
iid = TRUE, product.limit = FALSE
))
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, p.value = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1[,.SD,.SDcols = names(dt.ate2)], dt.ate2)
## manual calculation
suppressWarnings(dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.risk <- predict(e.S, newdata = iDT, times = tau, iid = TRUE,
cause = 1, product.limit = FALSE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(iPred.risk$absRisk)]
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(iPred.risk$absRisk.iid[,1,])]
iDT[, iid.IPTW := (X1test*Y/pi - mean(X1test*Y/pi))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y/pi^2))]
iDT[, iid.AIPTW := (X1test*Y/pi + r*(1-X1test/pi) - mean(X1test*Y/pi + r*(1-X1test/pi)))/.N]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*(Y-r)/pi^2))]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(iPred.risk$absRisk.iid[,1,], scale = 1-X1test/pi))]
return(iDT)
})))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.09315393, 0.37259431),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y/pi),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.08405157, 0.26793249),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*(event==1)*(time<=tau)/pi + r*(1-X1test/pi)),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.07847289, 0.47497984),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
unname(dtCf$iid.Gformula))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
unname(dtCf$iid.IPTW))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
unname(dtCf$iid.AIPTW))
## check standard error
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.Gformula^2),by="X1f"][[2]]),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.0315163, 0.2407176),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.IPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.03282852, 0.15183527),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.AIPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.03109236, 0.27245093),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.2794404, 0.1838809, 0.3965070),
"se" = c(0.2272017, 0.1549114, 0.2688568),
"lower" = c(-0.1658668, -0.1197399, -0.1304426),
"upper" = c(0.7247475, 0.4875017, 0.9234565),
"p.value" = c(0.2187263, 0.2352249, 0.1402693),
"quantileBand" = c(1.969141, 1.922815, 1.933780),
"adj.p.value" = c(0.2235, 0.2291, 0.1376),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(3.999770, 3.187715, 6.052789),
"se" = c(2.266232, 2.180114, 3.865633),
"lower" = c(0, 0, 0),
"upper" = c( 8.441504, 7.460660, 13.629289),
"p.value" = c(0.1856088, 0.3156261, 0.1911770),
"quantileBand" = c(1.971403, 1.958227, 1.974396),
"adj.p.value" = c(0.1872, 0.3072, 0.1973),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
})
## ** Censoring - manual computation, surv.type="survival"
n <- 1e2
set.seed(10)
dtS <- sampleData(n, outcome="competing.risks")
tau <- median(dtS$time)
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
dtS$C <- (dtS$time<=tau)*(dtS$event!=0)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
## GIVES WARNING
## Estimated risk outside the range [0,1].
## Possible cause: incorrect extrapolation, i.e., time and/or covariates used for the prediction differ from those used to fit the Cox models.
test_that("[ate] Censoring, competing risks (surv.type=\"survival\") - check vs. manual calculations", {
e.S <- CSC(Hist(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, surv.type = "survival")
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit"))
e.C <- coxph(Surv(time, event == 0) ~ X1f + X2, data = dtS, x = TRUE, y = TRUE)
## automatic calculation
suppressWarnings(e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE, cause = 1
))
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, p.value = TRUE, short = 2))
suppressWarnings(e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
censor = e.C,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE, cause = 1
))
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, p.value = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1, dt.ate2, tol = 1e-8)
## ## manual calculation ## ##
iPred.Cens <- predictCox(e.C, newdata = dtS, times = pmin(tau,dtS$time-1e-10), type = "survival", iid = TRUE)
iPred.Cens$survivalDiag <- diag(iPred.Cens$survival)
iPred.Cens$survivalDiag.iid <- sapply(1:n, function(iObs){iPred.Cens$survival.iid[,iObs,iObs]})
## augmentation term
jumpC <- sort(e.C$y[e.C$y[,"status"]==1,"time"])
indexJump <- prodlim::sindex(jump.time = jumpC, eval.times = pmin(tau,dtS$time))
pred.Risk_tau <- predict(e.S, newdata = dtS, times = tau, iid = TRUE, cause = 1, product.limit = FALSE)
pred.Risk_jump <- predict(e.S, newdata = dtS, times = jumpC, iid = TRUE, cause = 1, product.limit = FALSE)
pred.Surv_jump <- predictCox(e.S$models[["OverallSurvival"]], newdata = dtS, times = jumpC-1e-6, type = "survival", iid = TRUE)
pred.G_jump <- predictCox(e.C, newdata = dtS, times = jumpC-1e-10, type = "survival", iid = TRUE)
pred.dN_jump <- do.call(cbind,lapply(jumpC, function(iJ){(iJ == dtS$time)*(dtS$event==0)}))
pred.dLambda_jump <- predictCox(e.C, newdata = dtS, times = jumpC, type = "hazard", iid = TRUE)
pred.dM_jump <- pred.dN_jump-pred.dLambda_jump$hazard
term.SS <- -colCenter_cpp(pred.Risk_jump$absRisk,pred.Risk_tau$absRisk)/pred.Surv_jump$survival
integrand_jump <- term.SS*pred.dM_jump/pred.G_jump$survival
augTerm <- sapply(1:n, function(iObs){
if(indexJump[iObs]==0){
return(0)
}else{
return(sum(integrand_jump[iObs,1:indexJump[iObs]]))
}
})
augTermY1.iid <- calcIterm(factor = pred.dM_jump/(pred.Surv_jump$survival*pred.G_jump$survival),
iid = pred.Risk_tau$absRisk.iid,
indexJump = indexJump,
iid.outsideI = TRUE)
augTermY2.iid <- calcIterm(factor = -pred.dM_jump/(pred.G_jump$survival*pred.Surv_jump$survival),
iid = pred.Risk_jump$absRisk.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermSurv.iid <- calcIterm(factor = - term.SS * pred.dM_jump/(pred.Surv_jump$survival * pred.G_jump$survival),
iid = pred.Surv_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG1.iid <- calcIterm(factor = - term.SS * pred.dM_jump/pred.G_jump$survival^2,
iid = pred.G_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG2.iid <- calcIterm(factor = - term.SS / pred.G_jump$survival,
iid = pred.dLambda_jump$hazard.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,C,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.risk_tau <- predict(e.S, newdata = iDT, times = tau, iid = TRUE, cause = 1, product.limit = FALSE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(iPred.risk_tau$absRisk)]
iDT[, G := as.double(iPred.Cens$survivalDiag)]
iDT[, I := augTerm]
## Gformula ##
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(iPred.risk_tau$absRisk.iid[,1,])]
## IPTW
iDT[, iid.IPTW := (X1test*Y*C/(pi*G) - mean(X1test*Y*C/(pi*G)))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y*C/(pi^2*G)))]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
## AIPTW,AIPCW ##
iDT[, iid.AIPTW.ate := (X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi - mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi))/.N]
## outcome
iDT[, iid.AIPTW.outcome := rowMeans(rowMultiply_cpp(iPred.risk_tau$absRisk.iid[,1,], (1-X1test/pi)))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY1.iid,X1test/pi))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY2.iid,X1test/pi))]
## treatment
iDT[, iid.AIPTW.treatment := rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -(X1test/pi^2)*(Y*C/G - r + I)))]
## survival
iDT[, iid.AIPTW.survival := rowMeans(rowMultiply_cpp(augTermSurv.iid, X1test/pi))]
## censoring
iDT[, iid.AIPTW.censoring := rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG1.iid,X1test/pi))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG2.iid,X1test/pi))]
## total
iDT[, iid.AIPTW := iid.AIPTW.ate + iid.AIPTW.outcome + iid.AIPTW.treatment + iid.AIPTW.survival + iid.AIPTW.censoring]
return(iDT)
}))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.32111469, 0.99623763),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G)),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.31871273, 0.49590715),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.29549588, 0.9961723),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
dtCf$iid.Gformula)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
dtCf$iid.IPTW)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
dtCf$iid.AIPTW)
## check standard errors
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.Gformula^2)), by = "X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.05007538, 0.42597332),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.IPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.04961228, 0.15883446),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.AIPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.04803356, 0.316899),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.6751229, 0.1771944, 0.7006764),
"se" = c(0.4352720, 0.1655279, 0.3273409),
"lower" = c(-0.17799448, -0.14723439, 0.05910013),
"upper" = c(1.0000000, 0.5016232, 1.0000000),
"p.value" = c(0.12089283, 0.28440314, 0.03231356),
"quantileBand" = c(1.958676, 1.963257, 1.952228),
"adj.p.value" = c(0.1177, 0.2880, 0.0331),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(3.102436, 1.555969, 3.371188),
"se" = c(1.4694536, 0.5500765, 1.2732042),
"lower" = c(0.2223595, 0.4778388, 0.8757540),
"upper" = c(5.982512, 2.634099, 5.866623),
"p.value" = c(0.15249898, 0.31215422, 0.06254973),
"quantileBand" = c(1.955519, 1.941080, 1.992390),
"adj.p.value" = c(0.1553, 0.3178, 0.0605),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
})
## ** Censoring - manual computation, surv.type="hazard"
n <- 1e2
set.seed(10)
dtS <- sampleData(n, outcome="competing.risks")
tau <- median(dtS$time)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
test_that("[ate] Censoring, competing risks (surv.type=\"hazard\") - check vs. manual calculations", {
e.S <- CSC(Hist(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, surv.type = "hazard")
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit"))
e.C <- coxph(Surv(time, event == 0) ~ X1f + X2, data = dtS, x = TRUE, y = TRUE)
## automatic calculation
suppressWarnings(e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
iid = TRUE, product.limit = FALSE, cause = 1
))
dt.ate1 <- as.data.table(e.ate1)
suppressWarnings(e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
censor = e.C,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
iid = TRUE, product.limit = FALSE, cause = 1
))
dt.ate2 <- as.data.table(e.ate2)
expect_equal(ignore_attr=TRUE,dt.ate1, dt.ate2, tol = 1e-8)
})
## * [ate] Landmark analysis (time varying covariates)
cat("[ate] Landmark analysis \n")
fit <- coxph(Surv(time, status) ~ celltype+karno + age + trt, veteran)
vet2 <- survSplit(Surv(time, status) ~., veteran,
cut=c(60, 120), episode ="timegroup")
fitTD <- coxph(Surv(tstart, time, status) ~ celltype+karno + age + trt,
data= vet2,x=1)
test_that("[ate] landmark analyses", {
resVet <- ate(fitTD,formula=Hist(entry=tstart,time=time,event=status)~1,
data = vet2, treatment = "celltype", contrasts = NULL,
times=5,verbose=FALSE,
landmark = c(0,30,60,90), cause = 1, se = FALSE)
dt.resVet <- as.data.table(resVet)
dt.resVet$level <- factor(dt.resVet$level, levels = unique(dt.resVet$level))
setkeyv(dt.resVet,c("type","level"))
GS <- c(0.01773158, 0.02092285, 0.01489588, 0.02981096, 0.04098041, 0.04821725, 0.03463358, 0.06846231, 0.05589160, 0.06564467, 0.04741838, 0.09298832, 0.02633366, 0.03103968, 0.02217127, 0.04416996)
expect_equal(ignore_attr=TRUE,dt.resVet[type=="meanRisk",estimate], GS, tol = 1e-6)
GS <- c(0.02324883, 0.0272944, 0.01973769, 0.03865134, 0.03816002, 0.04472182, 0.0325225, 0.06317735, 0.00860208, 0.01011683, 0.00727539, 0.014359, 0.01491119, 0.01742742, 0.0127848, 0.02452601, -0.01464675, -0.01717757, -0.01246231, -0.02429234, -0.02955794, -0.03460499, -0.02524711, -0.04881836)
expect_equal(ignore_attr=TRUE,dt.resVet[type=="diffRisk",estimate], GS, tol = 1e-6)
GS <- c(2.31115372, 2.30452621, 2.32504339, 2.29654787, 3.15209352, 3.13746333, 3.18332093, 3.11926569, 1.48512762, 1.4835302, 1.488416, 1.48166834, 1.36386147, 1.3614353, 1.36914474, 1.3582411, 0.64259145, 0.64374629, 0.64016698, 0.64517198, 0.47115595, 0.47284384, 0.46756706, 0.47500549)
expect_equal(ignore_attr=TRUE,dt.resVet[type=="ratioRisk",estimate], GS, tol = 1e-6)
})
## * [ate] Case only
cat("[ate] Case only \n")
set.seed(11)
d <- sampleData(100, outcome = "survival")
d$id <- 1:NROW(d)
tau <- 3
## ** statistical model
e.cox <- coxph(Surv(time,event) ~ X1 + X2, x = TRUE, y = TRUE, data = d)
## ** ate: case only
test_that("Case only ate", {
## automatically
e.ateC <- ate(e.cox,
treatment = "X1",
data = d[X1 == "1"], time = tau, se = TRUE,
data.index = d[X1 == "1",id],
verbose = 0)
## manually
d0C <- copy(d[X1 == "1"])
d0C[, X1:= factor("0", levels = c("0","1"))]
d1C <- copy(d[X1 == "1"])
d1C[, X1:= factor("1", levels = c("0","1"))]
e.pred0C <- predictCox(e.cox, newdata = d0C, times = tau, average.iid = TRUE)
e.pred1C <- predictCox(e.cox, newdata = d1C, times = tau, average.iid = TRUE)
e2.ateC <- c(mean(1-e.pred0C$survival),
mean(1-e.pred1C$survival),
mean( (1-e.pred1C$survival)-(1-e.pred0C$survival)),
mean(1-e.pred1C$survival)/mean(1-e.pred0C$survival))
iid0C <- -e.pred0C$survival.average.iid
iid0C[d0C$id,] <- iid0C[d0C$id,] + (1-e.pred0C$survival-e2.ateC[1])/NROW(d0C)
iid1C <- -e.pred1C$survival.average.iid
iid1C[d1C$id,] <- iid1C[d1C$id,] + (1-e.pred1C$survival-e2.ateC[2])/NROW(d1C)
e2.se.ateC <- c(sqrt(sum(iid0C^2)),
sqrt(sum(iid1C^2)),
sqrt(sum((iid1C-iid0C)^2)),
sqrt(sum((iid1C/e2.ateC[1]-e2.ateC[2]*iid0C/e2.ateC[1]^2)^2))
)
expect_equal(ignore_attr=TRUE,as.data.table(e.ateC)$estimate, e2.ateC, tol = 1e-6)
expect_equal(ignore_attr=TRUE,as.data.table(e.ateC)$se, e2.se.ateC, tol = 1e-5)
})
## * [ate] Miscellaneous
cat("[ate] Miscellaneous \n")
## ** Pre-computation of iidCox does not affect the results
test_that("[ate] Cox model/G-formula - precompute iid", {
set.seed(10)
d <- sampleData(100)
e.CSC <- CSC(Hist(time,event)~X1+X2, data = d)
GS <- ate(e.CSC,
treatment = "X1", data = d, times = 1:5, cause = 1,
verbose = FALSE)
e.CSC <- iidCox(e.CSC)
test <- ate(e.CSC,
treatment = "X1", data = d, times = 1:5, cause = 1,
verbose = FALSE)
expect_equal(ignore_attr=TRUE,as.data.table(test), as.data.table(GS))
})
## ** Ate using list of formulae
## ** Specification of the censoring mecanism
test_that("[ate] Specification of the censoring mecanism",{
set.seed(10)
d <- sampleData(100)
d$allevent <- as.numeric(d$event>0)
expect_error(ate(event = Surv(time,allevent)~X1+X2,
censor = Surv(time,allevent)~X1,
treatment = X1 ~ X2,
data = d, times = 1:5, cause = 1,
verbose = FALSE))
expect_error(ate(event = Hist(time,event)~X1+X2,
censor = Surv(time,allevent)~X1,
treatment = X1 ~ X2,
data = d, times = 1:5, cause = 1,
verbose = FALSE))
## should run
test1 <- ate(event = Hist(time,event)~X1+X2,
censor = Surv(time,allevent==0)~X1,
treatment = X1 ~ X2,
data = d, times = 1:5, cause = 1,
verbose = FALSE)
})
## * [ate] Previous bug
cat("[ate] Previous bug \n")
## ** Results of ate depends on the number of timepoints
d <- sampleData(1000,outcome="survival")
test_that("[ate] Cox model/G-formula - one or several timepoints affects the results",{
fit <- coxph(Surv(time,event)~X1+X4+X7+X8,data=d,x=TRUE,y=TRUE)
a1 <- ate(fit,data=d,treatment="X1",time=5, verbose = FALSE)
a2 <- ate(fit,data=d,treatment="X1",time=5:7, verbose = FALSE)
expect_equal(ignore_attr=TRUE,a2$meanRisk[time==5,.(estimate,se)],
a1$meanRisk[time==5,.(estimate,se)])
expect_equal(ignore_attr=TRUE,a2$diffRisk[time==5,.(estimate,se)],
a1$diffRisk[,.(estimate,se)])
expect_equal(ignore_attr=TRUE,a2$ratioRisk[time==5,.(estimate,se)],
a1$ratioRisk[,.(estimate,se)])
})
## ** Before the first jump or at the first jump
set.seed(1)
d <- sampleData(100,outcome="survival")
tau <- c(d[event>0,min(time)] + c(-1e-5,+1e-5), median(d$time))
test_that("[ate] double robust estimator - before or at the first jump",{
## Error in rowSums(integrand.St[[iGrid]][, 1:beforeTau.nJumpC[iTau]]) :
## 'x' must be an array of at least two dimensions
fit <- ate(event = coxph(Surv(time,event)~X1+X4+X7+X8,data=d,x=TRUE,y=TRUE),
treatment = X1~X4,
censor = coxph(Surv(time,event==0)~X1+X4+X7+X8,data=d,x=TRUE,y=TRUE),
data=d, time=tau, se = TRUE, verbose = FALSE)
expect_output(print(fit))
})
## ** Multiple timepoints
set.seed(10)
n <- 1e2
dtS <- sampleData(n,outcome="competing.risks")
test_that("ate double robust estimator works with multiple timepoint",{
## TAG: Monday, Jul 8, 2019 9:09:36 PM (email subject Re: Branche ate for riskRegression ready)
e.CSC <- CSC(Hist(time, event) ~ X1 + X2 + X3,
data = dtS, surv.type = "hazard")
## previous error message
## Error in iidTotal[[contrasts[iC]]] + iid.treatment[[1]] : non-numeric argument to binary operator
e.ateRR <- ate(event = Hist(time,event) ~ X1 + X2 + X3,
treatment = glm(X1 ~ 1, data = dtS, family = binomial(link = "logit")),
censor = cph(Surv(time,event==0) ~ X1, data = dtS, x = TRUE, y = TRUE),
data = dtS, times = 3:4, verbose = FALSE, cause = 1
)
GS <- data.table("type" = c("meanRisk", "meanRisk", "meanRisk", "meanRisk", "diffRisk", "diffRisk", "ratioRisk", "ratioRisk"),
"estimator" = c("AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW"),
"time" = c(3L, 3L, 4L, 4L, 3L, 4L, 3L, 4L),
"level" = factor(c("0", "1", "0", "1", "0.1", "0.1", "0.1", "0.1"), levels = c("0","1","0.1")),
"estimate" = c(0.2585028, 0.5034093, 0.3052605, 0.5005653, 0.2449065, 0.1953049, 1.9474034, 1.6397974),
"se" = c(0.04636428, 0.15684698, 0.04902926, 0.15630839, 0.16327935, 0.16374745, 0.69821732, 0.57546242),
"lower" = c( 0.16763051, 0.19599485, 0.20916489, 0.19420653, -0.07511518, -0.12563425, 0.57892260, 0.51191176),
"upper" = c(0.3493751, 0.8108237, 0.4013561, 0.8069241, 0.5649281, 0.5162440, 3.3158842, 2.7676830),
"p.value" = c(NA, NA, NA, NA, 0.1336343, 0.2329791, 0.1748165, 0.2662254)
)
test <- as.data.table(e.ateRR)
test <- test[order(factor(test$type, levels = unique(test$type)),test$time)]
test$estimator <- as.character(test$estimator)
expect_equal(ignore_attr=TRUE,test, GS, tol = 1e-4)
e.ateRR <- ate(event = CSC(Hist(time,event) ~ X1 + X2 + X3, data = dtS),
treatment = "X1",
data = dtS, times = 3:4, verbose = FALSE, cause = 1
)
})
## ** Tied events
n <- 500
set.seed(10)
dt <- sampleData(n, outcome="survival")
dt$time <- round(dt$time,1)+0.1
dt$X1 <- factor(rbinom(n, prob = c(0.4) , size = 1), labels = paste0("T",0:1))
test_that("ate in presence of ties",{
## Brice: Tuesday, Sept 1, 2020 11:10
m.event <- coxph(Surv(time,event)~ X1,data=dt,x=TRUE,y=TRUE)
ateRobust <- ate(event = m.event,
treatment = "X1",
data = dt, times = 1,
cause = 1, product.limit = FALSE,
verbose = FALSE)
iPred <- predictCox(m.event, newdata = unique(dt[,.SD,.SDcols = "X1"]),
times = 1, se = TRUE, type = "survival", keep.newdata = TRUE)
iDT <- as.data.table(iPred)
setkeyv(iDT, "X1")
expect_equal(ignore_attr=TRUE,ateRobust$meanRisk$estimate, 1-iDT$survival, tol = 1e-7)
expect_equal(ignore_attr=TRUE,ateRobust$meanRisk$se, iDT$survival.se, tol = 1e-7)
})
dt <- data.table("time" = c( 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.005, 0.005, 0.005, 0.005, 0.005, 0.006, 0.006, 0.006, 0.006, 0.007, 0.007, 0.007, 0.007, 0.007, 0.007, 0.007, 0.007, 0.008, 0.008, 0.009, 0.009, 0.009, 0.010, 0.010, 0.010, 0.011, 0.011, 0.011, 0.012, 0.013, 0.014, 0.014, 0.015, 0.015, 0.016, 0.016, 0.016, 0.016, 0.016, 0.016, 0.016, 0.016, 0.017, 0.017, 0.017, 0.018, 0.018, 0.019, 0.019, 0.020, 0.021, 0.024, 0.024, 0.025, 0.025, 0.025, 0.025, 0.027, 0.027, 0.027, 0.029, 0.031, 0.032, 0.032, 0.033, 0.034, 0.034, 0.038, 0.040, 0.041, 0.042, 0.043, 0.043, 0.043, 0.048, 0.048, 0.049, 0.049, 0.049, 0.050, 0.052, 0.052, 0.053, 0.054, 0.056, 0.057, 0.059, 0.059, 0.060, 0.060, 0.061, 0.065, 0.066, 0.067, 0.067, 0.069, 0.069, 0.069, 0.070, 0.070, 0.071, 0.073, 0.074, 0.075, 0.076, 0.077, 0.080, 0.083, 0.090, 0.090, 0.095, 0.096, 0.101, 0.102, 0.105, 0.107, 0.109, 0.113, 0.114, 0.115, 0.117, 0.130, 0.131, 0.132, 0.135, 0.137, 0.139, 0.140, 0.144, 0.145, 0.148, 0.151, 0.152, 0.153, 0.155, 0.157, 0.159, 0.160, 0.160, 0.161, 0.163, 0.164, 0.167, 0.170, 0.179, 0.184, 0.186, 0.190, 0.199, 0.205, 0.209, 0.211, 0.213, 0.216, 0.219, 0.221, 0.222, 0.233, 0.238, 0.246, 0.251, 0.260, 0.262, 0.263, 0.273, 0.290, 0.296, 0.304, 0.310, 0.325, 0.333, 0.337, 0.371, 0.394, 0.422, 0.436, 0.444, 0.455, 0.467, 0.471, 0.486, 0.487, 0.518, 0.542, 0.555, 0.569, 0.590, 0.601, 0.602, 0.623, 0.635, 0.645, 0.649, 0.667, 0.672, 0.674, 0.712, 0.716, 0.731, 0.735, 0.745, 0.761, 0.782, 0.793, 0.800, 0.830, 0.857, 0.862, 0.887, 0.902, 0.964, 1.025, 1.110, 1.114, 1.169, 1.170, 1.179, 1.182, 1.189, 1.262, 1.274, 1.291, 1.297, 1.395, 1.462, 1.471, 1.516, 1.541, 1.547, 1.553, 1.621, 1.634, 1.667, 1.677, 1.681, 1.838, 1.872, 1.920, 2.022, 2.023, 2.035, 2.080, 2.101, 2.112, 2.313, 2.321, 2.343, 2.497, 2.507, 2.522, 2.572, 2.574, 2.591, 2.598, 2.668, 2.753, 2.908, 2.969, 2.971, 2.987, 3.006, 3.212, 3.243, 3.338, 3.586, 3.649, 3.750, 3.773, 3.977, 4.124, 4.173, 4.294, 4.298, 4.341, 4.645, 4.654, 4.722, 4.738, 4.906, 4.946, 5.109, 5.129, 5.294, 5.407, 5.601, 5.758, 5.807, 6.057, 6.068, 6.081, 6.200, 6.321, 6.411, 6.458, 6.494, 6.793, 6.882, 6.898, 6.977, 7.315, 7.426, 7.515, 7.643, 7.717, 8.024, 8.095, 8.203, 8.529, 8.591, 8.778, 9.919, 10.349, 11.364, 11.389, 11.471, 11.656, 12.082, 12.569, 12.902, 13.713, 14.194, 14.529, 14.596, 14.781, 14.881, 15.141, 15.342, 15.527, 15.854, 16.515, 16.999, 17.234, 17.287, 17.408, 18.575, 18.649, 19.089, 19.111, 19.265, 21.040, 21.416, 22.516, 22.571, 22.644, 22.913, 23.489, 24.640, 25.217, 25.250, 25.369, 25.524, 26.290, 27.315, 27.369, 28.162, 28.403, 28.548, 28.922, 28.954, 29.034, 29.634, 32.449, 32.667, 32.910, 33.566, 33.991, 34.409, 35.091, 35.177, 35.548, 37.138, 37.881, 40.640, 41.107, 42.194, 44.361, 48.559, 48.631, 49.985, 51.595, 52.309, 53.152, 54.773, 55.377, 56.479, 56.857, 59.070, 60.178, 60.677, 62.594, 64.336, 65.486, 65.750, 67.909, 69.234, 69.538, 69.764),
"cause" = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
"x1" = c(1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0),
"x2" = c(0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1),
"x3" = c(0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0),
"x4" = c( 1.34, 0.70, 3.06, 1.75, 2.65, -1.01, 1.84, 1.79, 1.20, 3.44, 0.03, 1.75, 1.73, 1.06, 1.52, 0.61, -0.64, -0.08, -0.14, 1.15, 0.33, 0.87, 2.07, 2.19, 0.28, 0.88, 0.84, 2.13, 1.84, 1.43, 0.91, 2.16, 0.47, 0.70, 2.06, 1.53, -0.75, 0.40, 1.62, 0.78, 2.34, 0.94, 0.66, 1.72, 0.50, 0.62, -0.55, 1.17, 0.56, 0.87, 1.77, 0.27, 1.23, -0.42, 2.46, 0.27, -0.29, 2.22, -0.24, 2.13, -0.88, 0.31, 0.23, 0.87, 0.70, 1.20, 2.27, 0.70, 0.53, 1.75, 1.94, 0.69, 0.09, 0.70, -0.10, 0.73, 0.31, 1.94, -0.50, 0.94, -0.21, 1.41, 1.19, -0.26, 0.35, 1.23, 0.76, 0.91, -0.07, 1.65, 0.02, -0.09, 0.67, 0.72, -0.23, 0.95, 0.81, 0.53, 1.77, 0.67, -0.26, 0.09, -0.56, 1.35, -0.12, 1.18, -0.55, 1.12, 0.03, 1.10, 0.87, 0.97, 1.16, -1.10, 0.42, 1.30, -0.42, 1.16, 1.96, 1.07, 0.39, 0.96, 0.93, 0.64, -0.80, 1.02, 0.63, 1.35, -0.84, 0.88, 0.57, 0.05, 0.14, 1.41, -1.71, 0.56, 0.87, 0.77, 0.03, 1.75, -0.54, 0.24, 0.32, 0.26, -0.39, 0.74, 0.19, 0.02, -0.31, 0.45, 0.35, 2.49, -0.73, 0.30, -0.49, -0.10, 1.24, 0.08, 0.09, -0.41, -0.02, 0.13, 1.13, 1.47, 1.15, 0.70, 0.60, -0.44, 0.98, -0.94, 0.07, 1.29, -0.71, 0.34, 0.10, 1.32, -0.10, -0.08, -0.74, 0.77, -0.09, -0.49, 1.09, 0.23, -0.69, 1.09, 0.44, 0.33, -0.60, 0.93, -0.28, -1.01, 0.34, 1.62, 0.57, 1.20, 0.65, -0.38, 0.36, 0.66, 0.73, -0.04, 0.72, -0.26, -0.26, 0.63, -0.02, 0.51, 0.37, 0.71, -0.01, 0.06, -0.40, 0.44, 1.08, -0.22, 0.40, 0.47, 0.66, -0.93, 0.16, 0.89, 1.40, -0.18, -0.05, 0.16, -0.95, -1.49, 1.12, 0.87, -0.56, 1.00, -0.51, 0.78, 0.17, 0.46, 1.37, -0.15, -0.82, 0.79, -0.35, -0.67, -0.48, -0.22, -0.50, 1.67, -0.83, -0.97, 1.27, -0.86, 0.60, 0.30, 0.46, 0.23, -0.41, 1.36, 0.53, -1.60, 0.77, 0.32, -0.88, 0.65, 0.07, 0.51, 1.42, -0.39, 0.15, 0.34, -0.09, 0.31, 0.67, -1.20, 1.48, -1.30, 0.92, 1.34, -0.96, 0.45, 0.32, -1.44, 0.68, 0.35, -0.46, -0.16, 0.04, 0.23, 0.23, 0.48, -0.20, 0.05, -0.27, -0.50, -0.02, -0.27, 0.23, 0.10, 0.70, 0.06, -0.66, 0.39, 0.19, 0.83, -1.04, 0.21, -0.53, -0.55, -1.88, 0.66, -1.01, -1.02, -1.46, -1.41, -0.84, -0.66, -0.13, -1.11, -0.03, -0.01, 0.48, -0.83, 0.00, 1.10, 0.12, -0.60, -0.64, -0.65, -1.11, -0.20, -0.73, -0.11, 0.74, -1.80, -0.34, -0.01, -0.20, -0.76, -1.63, 0.17, -0.87, 0.89, -0.71, -0.67, 0.06, -0.30, 0.50, 0.30, -1.29, -1.05, -0.12, -0.82, -0.79, -0.15, 0.74, -0.64, -0.76, 0.21, 0.65, 0.39, -0.49, 0.40, -0.58, -0.66, 0.29, 0.87, 0.19, -0.55, -1.08, 0.09, -0.74, -2.36, 0.14, 1.27, -0.39, 0.10, -0.91, -1.15, -1.15, -0.74, -0.64, -1.29, 0.07, -0.77, -0.82, -0.10, -0.47, -0.29, -1.55, -0.25, -0.42, -0.36, -1.00, -1.36, -1.78, 0.50, -0.91, -0.28, 0.35, -0.84, -1.85, 0.65, -0.90, 0.61, 1.01, -0.90, 0.11, 0.20, 0.50, -0.87, -1.91, -1.95, -0.43, 0.04, -0.98, -1.68, -1.29, -1.77, -0.17, -1.38, -0.64, -1.26, -0.96, -0.11, -0.59, -1.31, -0.12, -1.11, 0.11, -1.53, -1.98, 0.27, -0.17, -0.51, -2.15, 0.65, 0.13, -0.50, -0.47, -2.45, -0.77, -1.51, -0.02, -0.86, -1.75, -1.49, -0.16, -0.47, -1.78, -0.77, -0.03, -0.70, -0.92, -0.10, -0.37, -0.38, -1.56, 0.40, -0.19, -2.58, 0.66, -1.72, -1.17, -0.55, -1.25, -0.69, -1.90, -0.76, -1.44, -2.54, -1.60, -2.17, -1.52, -0.27, -2.74, -1.45, -0.41, -1.17, -1.80, 1.32, -0.41, -1.30, 0.27, -0.03, -1.87, -0.60, -0.85, -1.16, -1.12, -1.18, -1.61, -1.43, -0.74, -2.19, -0.77, -1.69, -1.53, -1.12, -1.36, -0.05, -1.31, -1.86),
"x5" = c( 1.90, -0.33, 0.64, 0.62, 0.56, 1.35, 1.21, -0.58, -0.54, 0.74, 1.22, 2.29, -0.39, 1.53, 1.33, 2.36, 1.50, 0.44, 2.17, 1.40, 0.15, 1.19, 1.83, 0.35, 0.78, 1.45, 0.72, 2.33, 1.05, 0.87, -0.01, 0.40, 0.69, 1.13, 0.38, -0.31, 2.88, 1.20, 0.99, 0.48, 0.49, 0.79, 0.45, 0.10, 0.75, 0.12, 1.54, 0.98, -0.36, 0.81, 0.00, 0.14, 0.30, 1.40, -0.29, 0.19, 0.83, 0.63, 0.64, 0.16, 1.12, 1.34, 1.77, 1.32, -0.49, 1.53, 0.00, 1.66, 0.88, 0.23, -0.22, -1.02, 1.29, 0.39, -0.17, -0.59, 0.44, 0.52, 0.66, 2.13, 0.97, 1.50, -0.67, 0.53, 0.88, -0.88, -0.60, 0.71, 1.24, 0.34, 1.43, 0.25, 1.14, 0.19, 0.89, 0.87, 0.15, -0.41, -0.70, 0.85, -0.03, -1.06, 0.35, -0.62, 0.82, 1.23, -0.30, 1.44, 0.41, -0.57, -1.61, 0.17, -0.91, 1.03, 1.21, 0.36, 0.53, -0.99, -1.03, 0.66, 1.41, -0.77, 0.74, -0.39, 1.63, -1.51, 0.60, -0.02, 0.06, 0.54, 1.23, -0.19, 0.21, 0.11, 0.13, -0.08, 0.74, 0.11, 0.15, 0.49, 0.74, 0.37, 0.28, 0.24, -0.22, -0.73, -0.41, 0.60, 0.24, 0.49, 0.64, -0.66, 0.38, 0.80, 1.00, 1.25, 0.05, 1.38, 0.98, 0.83, 0.76, -0.68, -0.64, 0.11, -1.05, 0.14, -0.37, 0.50, -0.17, 1.11, -1.83, 0.53, 0.95, 0.57, 0.37, 0.80, -0.39, 0.30, 0.90, 0.43, 0.47, 0.81, 0.73, 0.07, 1.59, 0.26, 0.58, -1.46, 1.26, 0.41, 0.28, 1.83, -0.58, -0.26, -0.87, -1.10, -1.19, 0.53, 0.18, 0.54, 0.68, 0.37, -1.66, 0.35, 1.25, -0.27, -0.57, -0.32, 0.90, 0.35, -0.33, 0.16, -0.22, -0.09, -1.29, -0.27, -1.28, -0.93, -1.18, -0.66, 0.10, 0.07, -1.39, -0.37, 0.89, 0.02, 0.68, 0.22, -0.07, -1.07, -0.90, 0.31, -0.40, -0.49, 0.23, -0.16, 0.33, -0.10, -0.64, 0.33, 0.70, 0.66, 0.18, 0.28, -0.96, 0.25, -0.07, 0.94, -1.18, -0.16, -0.33, 1.21, -0.91, 0.41, -1.05, 0.38, -1.00, 0.41, -0.80, -0.64, 1.45, 0.22, 0.76, 0.12, -0.08, -0.31, -0.11, -1.09, 0.53, 0.28, -1.05, 0.72, -0.68, 0.33, -0.62, -2.08, -0.51, -0.41, 0.48, 1.17, -0.48, -0.90, 0.16, 1.47, 0.87, -1.06, -1.17, -2.10, 0.77, -0.93, 0.09, 0.18, -0.30, -0.97, -0.02, -1.00, -0.69, -1.24, -0.57, -0.99, 0.59, 0.44, 1.04, 0.69, -0.69, -0.26, 0.91, -1.30, -0.27, 0.85, 1.59, 1.62, 0.75, 0.61, 0.32, 0.37, -0.57, -0.22, -1.98, 0.51, 0.74, -0.01, -1.84, -0.13, -0.40, -0.79, 0.14, 0.09, 0.23, -1.15, -1.39, 1.03, 1.70, 0.01, -1.05, -0.09, 1.61, -1.91, 0.13, -0.81, -1.01, 1.14, -1.07, -0.22, -0.27, -1.61, -0.75, -0.96, 0.54, 0.98, -0.86, 0.58, -0.34, -0.58, -0.26, -1.61, -0.38, -0.19, 0.33, 0.25, -0.15, -0.22, -0.04, -1.49, 0.30, -0.29, 0.03, -1.06, -0.11, 0.84, -1.18, -0.66, 0.46, 0.20, -0.56, 1.07, 0.52, -1.04, 0.31, -0.47, -1.17, -0.96, -0.94, -0.84, -1.18, 0.35, -0.99, 0.42, 0.07, 0.16, 0.78, -0.94, -1.57, 0.49, -1.41, -1.65, -0.86, -0.87, 2.31, -0.53, -0.50, -0.68, -1.33, -0.04, -0.03, -0.15, -2.59, -1.10, 1.44, 0.89, -0.75, -0.86, 0.16, 0.76, -1.10, 0.96, -1.74, 1.01, -0.77, 0.51, -0.18, 0.62, -1.75, -0.31, -1.92, -1.35, -1.17, 0.39, 0.26, -1.34, -1.79, -0.99, 0.27, -2.55, -0.02, -1.14, -0.17, -0.38, -0.78, -0.18, -0.34, 0.04, -0.80, -0.64, -1.64, -0.69, -0.17, 0.50, 0.03, 1.10, 0.64, -1.55, -0.68, -1.09, -0.50, -1.49, -1.55, 1.12, -2.14, -1.92, -0.46, 0.38, -0.63, 0.92, -0.79, 1.39, -0.65, -0.85, -0.03, -1.70, -1.05, -1.42, 1.53, -1.14, -0.38, -1.92, -1.17, -2.71, -1.36, 0.16, -0.32, -0.94, 1.33, -0.20, -0.67, -0.73, -2.00, -2.05, -1.98, 0.18, -1.57, -0.53, -1.47, 0.72, 0.04, -0.71, -1.16, -1.56, -1.94, -0.32),
"group" = c("1", "0", "0", "1", "0", "1", "1", "0", "1", "1", "1", "1", "1", "0", "1", "0", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "1", "1", "1", "0", "1", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1", "1", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "1", "0", "1", "0", "1", "0", "0", "1", "0", "1", "1", "1", "0", "1", "1", "0", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "0", "1", "1", "1", "1", "0", "0", "1", "1", "0", "1", "0", "0", "1", "1", "0", "0", "1", "0", "0", "0", "1", "1", "1", "0", "1", "1", "0", "1", "0", "1", "0", "1", "0", "0", "0", "1", "0", "1", "1", "1", "1", "1", "0", "1", "0", "1", "1", "0", "1", "0", "1", "1", "0", "0", "0", "0", "1", "1", "0", "0", "0", "0", "1", "0", "0", "0", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1", "0", "0", "0", "1", "0", "0", "1", "1", "0", "0", "1", "0", "1", "0", "0", "1", "1", "1", "1", "1", "0", "0", "1", "0", "0", "0", "0", "1", "0", "0", "1", "1", "1", "0", "1", "0", "0", "1", "0", "1", "0", "1", "1", "0", "0", "1", "1", "0", "1", "1", "1", "0", "0", "1", "1", "0", "0", "0", "1", "1", "0", "0", "1", "0", "1", "0", "1", "1", "0", "1", "1", "0", "0", "0", "1", "0", "0", "0", "1", "0", "1", "0", "0", "0", "0", "0", "0", "0", "1", "1", "0", "0", "1", "1", "0", "1", "1", "1", "0", "1", "1", "0", "1", "0", "1", "1", "1", "1", "0", "0", "1", "1", "1", "1", "0", "1", "1", "0", "0", "1", "0", "1", "0", "0", "1", "0", "0", "0", "1", "1", "1", "0", "1", "0", "1", "1", "0", "0", "0", "0", "0", "0", "1", "1", "1", "1", "0", "0", "0", "1", "0", "0", "0", "1", "1", "0", "1", "1", "1", "0", "1", "1", "0", "0", "0", "1", "1", "1", "0", "1", "0", "0", "0", "1", "0", "1", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "1", "1", "1", "1", "0", "0", "1", "1", "1", "0", "1", "0", "1", "1", "0", "1", "1", "1", "0", "0", "1", "1", "0", "0", "0", "0", "1", "0", "1", "1", "1", "1", "1", "0", "1", "0", "0", "0", "1", "1", "1", "1", "0", "1", "0", "0", "0", "1", "0", "1", "1", "1", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "1", "1", "0", "0", "1", "1", "1", "1", "1", "0", "1", "1", "0", "0", "0", "0", "1", "0", "0", "1", "0", "0", "0", "1", "1", "0", "0", "0", "1", "0", "1", "0", "0", "1", "0", "1", "0", "0", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "1", "0", "1", "0", "0", "1", "0", "0"))
dt$group <- as.factor(dt$group)
test_that("no error", { ## firs censoring happens at 1 which used to cause problem when evaluating at Gc at t-
## ** Between first and second censoring time
## ISSUE 18 (Github) ate function produces errors and NaN values when using method = "AIPTW"
## RobinDenz1
e.outcome <- coxph(Surv(time, cause) ~ x1 + x2 + x4 + x5 + group, data=dt, x=TRUE)
e.censoring <- coxph(Surv(time, cause==0) ~ group, data=dt, x=TRUE)
e.treatment <- glm(group ~ 1, data=dt, family="binomial")
predS <- predictCox(e.outcome, newdata = dt, time = 0.15)$survival
## range(predS)
testBefore1 <- ate(event = e.outcome,
treatment = e.treatment,
censor = e.censoring,
data = dt,
times=0.9,
se=TRUE, verbose = FALSE,
estimator="AIPTW")
expect_equal(ignore_attr=TRUE,as.data.table(testBefore1)$estimate, c(0.5701832, 0.6732129, 0.1030297, 1.1806959), tol = 1e-6)
expect_equal(ignore_attr=TRUE,as.data.table(testBefore1)$se, c(0.02590195, 0.02512034, 0.02773938, 0.05370323), tol = 1e-6)
## for times > 1 used to return an error
testAfter1 <- ate(event = e.outcome,
treatment = e.treatment,
censor = e.censoring,
data = dt,
times=1.5,
se=TRUE, verbose = FALSE,
estimator="AIPTW")
## for times > 1 it returns NaN only
expect_equal(ignore_attr=TRUE,as.data.table(testAfter1)$estimate, c(0.60287336, 0.69921301, 0.09633966, 1.15980082), tol = 1e-6)
expect_equal(ignore_attr=TRUE,as.data.table(testAfter1)$se, c(0.02559758, 0.02458960, 0.02724511, 0.04936567), tol = 1e-6)
})
# }}}
######################################################################
### test-ate.R ends here
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### test-ate.R ---
#----------------------------------------------------------------------
## Author: Thomas Alexander Gerds
## Created: Jun 25 2024 (09:21)
## Version:
## Last-Updated: Jun 25 2024 (09:24)
## By: Thomas Alexander Gerds
## Update #: 2
#----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
#----------------------------------------------------------------------
##
### Code:
library(testthat)
library(data.table)
library(mets)
library(riskRegression)
library(survival)
## * Simulate data
set.seed(10)
n <- 250
tau <- 1:5
d <- sampleData(n, outcome = "competing.risks")
dFull <- d[event!=0] ## remove censoring
dSurv <- d[event!=2] ## remove competing risk
# {{{ no censoring
test_that("wglm - no censoring",{
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = dFull)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event)~X1 + X8,
time = tau[5], data = dFull))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
## ate
test.ate <- ate(test, data = dFull, times = tau, treatment = "X1", verbose = FALSE)
GS.ate <- logitATE(formula = Event(time,event)~X1 + X8,
time = tau[5], data = dFull, treat.model = X1~1)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,se], unname(GS.ate$se.difriskG),
tolerance = 1e-5)
})
# }}}
# {{{ right censoring (but no competing risks)
test_that("wglm - censoring",{
#### no covariate in censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = dSurv, product.limit = TRUE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event)~X1 + X8,
cens.model = ~1,
time = tau[5], data = dSurv, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
## check weights
e.KM <- as.data.table(predictCoxPL(coxph(Surv(time,event==0) ~ 1, data = dSurv),
type = c("hazard","survival")))
## ate
test.ate <- ate(test, data = dSurv, times = tau, treatment = "X1", verbose = FALSE)
GS.ate <- suppressWarnings(logitATE(formula = Event(time,event)~X1 + X8, cens.model = ~1, cens.code = 0, cause = 1,
time = tau[5], data = dSurv, treat.model = X1~1, binreg = FALSE))
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,se], unname(GS.ate$se.difriskG),
tolerance = 1e-5)
#### stratified censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1,
times = tau, data = dSurv, product.limit = FALSE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1,
time = tau[5], data = dSurv, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
#### censoring model with continuous covariate ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1+X8,
times = tau, data = dSurv, product.limit = FALSE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1+X8,
time = tau[5], data = dSurv, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
})
# }}}
# {{{ competing risks
test_that("wglm - competing risks",{
#### no covariate in censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = d, product.limit = TRUE, cause = 1)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~1,
time = tau[5], data = d, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"], tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
## ate
test.ate <- ate(test, data = d, times = tau, treatment = "X1", verbose = FALSE)
GS.ate <- suppressWarnings(logitATE(formula = Event(time,event)~X1 + X8, cens.model = ~1, cens.code = 0, cause = 1,
time = tau[5], data = d, treat.model = X1~1, binreg = FALSE))
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,estimate], unname(GS.ate$difriskG),
tolerance = 1e-5)
expect_equal(ignore_attr=TRUE,test.ate$diffRisk[5,se], unname(GS.ate$se.difriskG),
tolerance = 1e-5)
#### stratified censoring model ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1,
times = tau, data = d, product.limit = FALSE, cause = 1)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1,
time = tau[5], data = d, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"],
tolerance = 1e-5)
## expect_equal(summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
#### censoring model with continuous covariate ####
test <- wglm(regressor.event = ~ X1 + X8, formula.censor = Surv(time,event==0) ~ X1+X8,
times = tau, data = d, product.limit = FALSE)
GS <- suppressWarnings(logitIPCW(formula = Event(time,event) ~ X1 + X8,
cens.model = ~X1+X8,
time = tau[5], data = d, cens.code = 0, cause = 1))
expect_equal(ignore_attr=TRUE,coef(test, time = tau[5]), summary(GS)$coef[,"Estimate"],
tolerance = 1e-5)
## expect_equal(summary(test, print = FALSE)[[5]][,"Std. Error"], summary(GS)$coef[,"Std.Err"], tolerance = 1e-5)
})
# }}}
# {{{
verbose <- FALSE
calcIterm <- function(factor, iid, indexJump, iid.outsideI){
n <- length(indexJump)
if(iid.outsideI){ ## compute iid int factor
ls.I <- lapply(1:n, function(iObs){ ## iObs <- 2
iIID <- iid[,1,iObs]
iFactor <- factor[iObs,1:indexJump[iObs]]
if(indexJump[iObs]==0){
return(rep(0,n))
}else {
return(iIID*sum(iFactor))
}
})
} else { ## compute int iid * factor
ls.I <- lapply(1:n, function(iObs){ ## iObs <- 2
iIID <- iid[,1:indexJump[iObs],iObs]
iFactor <- factor[iObs,1:indexJump[iObs]]
if(indexJump[iObs]==0){
return(rep(0,n))
}else if(indexJump[iObs]==1){
return(iIID*iFactor)
}else{
return(rowSums(rowMultiply_cpp(iIID, iFactor)))
}
})
}
return(do.call(cbind,ls.I))
}
## ** Ate using list of formulae
test_that("[ate] using list of formulae",{
set.seed(10)
d <- sampleData(100)
expect_output(print(ate(list(Hist(time,event)~X1+X2,
Hist(time,event)~X1+X2),
treatment = "X1", data = d, times = 1:5, cause = 1,
verbose = FALSE)))
})
## * [ate] G-formula only
cat("[ate] G-formula only \n")
## ** data
set.seed(10)
n <- 5e1
dtS <- sampleData(n,outcome="survival")
dtS$time <- round(dtS$time,1)
dtS$X1 <- factor(rbinom(n, prob = c(0.3,0.4) , size = 2), labels = paste0("T",0:2))
## ** Compare to fix value / explicit computation
test_that("[ate] G-formula,survival - compare to fix values / explicit computation",{
seqTime <- c(1,5,10)
fit <- cph(formula = Surv(time,event)~ X1+X2,data=dtS,y=TRUE,x=TRUE)
ateFit <- ate(fit, data = dtS, treatment = "X1", contrasts = NULL,
times = seqTime, band = FALSE, verbose = FALSE)
dt.ateFit <- as.data.table(summary(ateFit, band = TRUE, p.value = FALSE, short = 2))
## point estimate
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0" & type == "meanRisk",estimate],
c(0.05842145, 0.46346908, 0.66107744),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0" & type == "meanRisk",lower],
c(0.0000000, 0.2756147, 0.4670461),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0" & type == "meanRisk",upper],
c(0.1178148, 0.6513235, 0.8551088),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk",estimate],
c(0.0006541715, 0.0037795576, 0.0040931551),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk",lower],
c(-0.04241346, -0.24364096, -0.26383983),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk",upper],
c(0.0437218, 0.2512001, 0.2720261),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk", lowerBand],
c(-0.04230655, -0.24302677, -0.26317472),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "diffRisk", upperBand],
c(0.04361489, 0.25058589, 0.27136103),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk",estimate],
c(1.011197, 1.008155, 1.006192),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk",lower],
c(0.2745873, 0.4723104, 0.5997906),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk",upper],
c(1.747808, 1.543999, 1.412593),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk", lowerBand],
c(0.26976028, 0.46879896, 0.5971274),
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dt.ateFit[level == "T0.T1" & type == "ratioRisk", upperBand],
c(1.75263463, 1.5475109, 1.41525588),
tol = 1e-6)
## standard error
ATE <- list()
ATE.iid <- list()
for(iT in c("T0","T1","T2")){ ## iT <- "T0"
## risk
newdata0 <- copy(dtS)
newdata0$X1 <- iT
fit <- cph(formula = Surv(time,event)~ X1+X2,data=dtS,y=TRUE,x=TRUE)
resPred <- predictCox(fit, newdata = newdata0, time = seqTime, iid = TRUE)
ATE[[iT]] <- colMeans(1-resPred$survival)
ATE.iid_term1 <- apply(-resPred$survival.iid,1,rowMeans)
ATE.iid_term2 <- apply(1-resPred$survival, 1, function(x){x-ATE[[iT]]})/n
ATE.iid[[iT]] <- t(ATE.iid_term1) + t(ATE.iid_term2)
ATE.se <- sqrt(apply(ATE.iid[[iT]]^2, 2, sum))
ATE.lower <- ATE[[iT]]+ qnorm(0.025) * ATE.se
ATE.upper <- ATE[[iT]] + qnorm(0.975) * ATE.se
expect_equal(ignore_attr=TRUE,pmax(0,ATE.lower), dt.ateFit[level == iT,lower])
expect_equal(ignore_attr=TRUE,pmin(1,ATE.upper), dt.ateFit[level == iT,upper])
}
## difference in risk
diffATE <- ATE[["T1"]]-ATE[["T0"]]
diffATE.iid <- ATE.iid[["T1"]]-ATE.iid[["T0"]]
diffATE.se <- sqrt(apply(diffATE.iid^2, 2, sum))
diffATE.lower <- diffATE + qnorm(0.025) * diffATE.se
diffATE.upper <- diffATE + qnorm(0.975) * diffATE.se
expect_equal(ignore_attr=TRUE,diffATE.lower, dt.ateFit[level == "T0.T1" & type == "diffRisk", lower])
expect_equal(ignore_attr=TRUE,diffATE.lower, c(-0.04241346, -0.24364096, -0.26383983), tol = 1e-6)
expect_equal(ignore_attr=TRUE,diffATE.upper, dt.ateFit[level == "T0.T1" & type == "diffRisk", upper])
expect_equal(ignore_attr=TRUE,diffATE.upper, c(0.0437218, 0.2512001, 0.2720261), tol = 1e-6)
## ratio of the risks
ratioATE <- ATE[["T1"]]/ATE[["T0"]]
ratioATE.iid <- rowScale_cpp(ATE.iid[["T1"]],ATE[["T0"]])-rowMultiply_cpp(ATE.iid[["T0"]], ATE[["T1"]]/ATE[["T0"]]^2)
expect_equal(ignore_attr=TRUE,unname(ATE.iid[["T1"]]),unname(ateFit$iid$GFORMULA$T1))
ratioATE.se <- sqrt(rowSums(ratioATE.iid^2))
ratioATE.se <- sqrt(colSums(ratioATE.iid^2))
ratioATE.lower <- ratioATE + qnorm(0.025) * ratioATE.se
ratioATE.upper <- ratioATE + qnorm(0.975) * ratioATE.se
expect_equal(ignore_attr=TRUE,ratioATE.lower, dt.ateFit[level == "T0.T1" & type == "ratioRisk", lower])
expect_equal(ignore_attr=TRUE,ratioATE.lower, c(0.2745873, 0.47231036, 0.59979056), tol = 1e-6)
expect_equal(ignore_attr=TRUE,ratioATE.upper, dt.ateFit[level == "T0.T1" & type == "ratioRisk", upper])
expect_equal(ignore_attr=TRUE,ratioATE.upper, c(1.7478076, 1.5439995, 1.41259273))
})
## ** strata argument
test_that("[ate] G-formula,survival - strata argument",{
## Cox model
e.coxph <- coxph(Surv(time, event) ~ X1, data = dtS,
x = TRUE, y = TRUE)
outATE <- ate(e.coxph, data = dtS, treatment = NULL, strata = "X1", time = 1,
product.limit = FALSE, verbose = FALSE, band = TRUE)
dt.outATE <- as.data.table(outATE)
outPred <- predictCox(e.coxph,
newdata = dtS,
times = 1,
iid = TRUE,
se = TRUE,
keep.newdata = TRUE,
type = "survival")
GS.se <- as.data.table(outPred)[,.SD[1],by = "X1"]
setkeyv(GS.se, cols = "X1")
expect_equal(ignore_attr=TRUE,1-GS.se$survival, dt.outATE[type=="meanRisk",estimate])
expect_equal(ignore_attr=TRUE,GS.se$survival.se, dt.outATE[type=="meanRisk",se])
## Stratified Cox model
e.coxph <- coxph(Surv(time, event) ~ strata(X1), data = dtS,
x = TRUE, y = TRUE)
outATE <- ate(e.coxph, data = dtS, treatment = NULL, strata = "X1", time = 3,
verbose = FALSE, band = TRUE)
dt.outATE <- as.data.table(outATE)
outPred <- predictCox(e.coxph,
newdata = dtS,
times = 3,
se = TRUE,
keep.newdata = TRUE,
type = "survival")
GS.se <- as.data.table(outPred)[,.SD[1],by = "X1"]
setkeyv(GS.se, cols = "X1")
expect_equal(ignore_attr=TRUE,1-GS.se$survival, dt.outATE[type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,GS.se$survival.se, dt.outATE[type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,GS.se$survival.se, dt.outATE[type == "meanRisk",se])
})
## * [ate] Logistic regression
cat("[ate] Logistic regression \n")
n <- 100
set.seed(10)
dtB <- sampleData(n, outcome="binary")
## dtB <- data.table(X1 = rbinom(n,size =1,prob=0.5),
## X2 = rbinom(n,size =1,prob=0.5))
## dtB$Y <- rbinom(n, size = 1, prob = expit(dtB$X1+dtB$X2))
## dtB$X1 <- as.factor(dtB$X1)
## dtB$X2 <- as.factor(dtB$X2)
test_that("[ate] logistic regression - compare to lava",{
fitY <- glm(formula = Y ~ X1 + X2, data=dtB, family = "binomial")
fitT <- glm(formula = X1 ~ X2, data=dtB, family = "binomial")
## G-formula
e0.ate <- ate(fitY, data = dtB, treatment = "X1",
se = TRUE, iid = TRUE, B = 0, verbose = FALSE)
e.ate <- ate(fitY, data = dtB, treatment = "X1",
times = 5,
se = TRUE, iid = TRUE, B = 0, verbose = FALSE)
dt.ate <- as.data.table(e.ate)
e.lava <- lava::estimate(fitY, function(p, data){
a <- p["(Intercept)"] ; b <- p["X11"] ; c <- p["X21"] ;
R.X11 <- lava::expit(a + b + c * (data[["X2"]]=="1"))
R.X10 <- lava::expit(a + c * (data[["X2"]]=="1"))
list(risk0=R.X10,risk1=R.X11,riskdiff=R.X11-R.X10)},
average=TRUE)
expect_equal(ignore_attr=TRUE,unname(e.lava$coef), dt.ate[type %in% c("meanRisk","diffRisk"),estimate],tolerance=0.001)
expect_equal(ignore_attr=TRUE,unname(e.lava$vcov[1:2,1:2]), unname(crossprod(do.call(cbind,e.ate$iid$GFORMULA))),tolerance=0.001)
expect_equal(ignore_attr=TRUE,unname(sqrt(diag(e.lava$vcov))), c(dt.ate[type %in% c("meanRisk","diffRisk"),se]),tolerance=0.001)
## AIPTW
e.ate2 <- ate(fitY,
treatment = fitT,
data = dtB,
times = 5,
se = TRUE, iid = TRUE, B = 0, verbose = FALSE
)
dt.ate2 <- as.data.table(e.ate2)
dtB$Y0 <- 0
dtB$Y1 <- 1
## iid ate
iPredT <- predict(fitT, type = "response")
dtBC <- rbind(cbind(dtB[,.(Y,X2)], X1 = factor(0, levels = levels(dtB$X1)), X1test = dtB$X1=="0", pi = 1-iPredT),
cbind(dtB[,.(Y,X2)], X1 = factor(1, levels = levels(dtB$X1)), X1test = dtB$X1=="1", pi = iPredT))
dtBC$r <- predict(fitY, newdata = dtBC, type = "response")
dtBC[, ate := Y*X1test/pi + r*(1-X1test/pi)]
dtBC[, ate.iid := (ate-mean(ate))/.N, by = "X1"]
## iid outcome
iid.risk <- attr(predictRisk(fitY, newdata = dtBC, iid = TRUE),"iid")
nuisanceY.iid <- rowMultiply_cpp(iid.risk, scale = (1-dtBC$X1test/dtBC$pi))
dtBC$AnuisanceY.iid <- c(rowMeans(nuisanceY.iid[,dtBC$X1=="0"]),rowMeans(nuisanceY.iid[,dtBC$X1=="1"]))
## iid treatment
iid.pi <- attr(predictRisk(fitT, newdata = dtBC, iid = TRUE),"iid")
nuisanceT.iid <- rowMultiply_cpp(iid.pi, scale = (-1)^(dtBC$X1=="1")*dtBC$X1test*(dtBC$Y-dtBC$r)/dtBC$pi^2)
dtBC$AnuisanceT.iid <- c(rowMeans(nuisanceT.iid[,dtBC$X1=="0"]),rowMeans(nuisanceT.iid[,dtBC$X1=="1"]))
## global
expect_equal(ignore_attr=TRUE,dtBC[, mean(ate), by = "X1"][[2]],
dt.ate2[type=="meanRisk",estimate])
expect_equal(ignore_attr=TRUE,dtBC[, sqrt(sum( (ate.iid+AnuisanceY.iid+AnuisanceT.iid)^2 )), by = "X1"][[2]],
dt.ate2[type=="meanRisk",se])
## butils:::object2script(dt.ate2)
## GS <- data.table("type" = c("ate", "ate", "diffAte", "ratioAte"),
## "level" = c("0", "1", "0.1", "0.1"),
## "time" = c(5, 5, 5, 5),
## "value" = c(0.4167808, 0.7341667, 0.3173858, 1.7615174),
## "se" = c(0.05227194, 0.13439533, 0.14429419, 0.39122437),
## "lower" = c(0.31432969, 0.47075666, 0.03457442, 0.99473171),
## "upper" = c(0.5192320, 0.9975767, 0.6001973, 2.5283031),
## "estimator" = c("AIPTW", "AIPTW", "AIPTW", "AIPTW"))
})
## * [ate] Survival case
cat("[ate] Survival case \n")
## ** no censoring - manual computation
## *** Data
n <- 5e1
set.seed(10)
dtS <- sampleData(n,outcome="survival")
tau <- median(dtS$time)
dtS$event <- 1
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
## *** check
test_that("[ate] no censoring, survival - check vs. manual calculations", {
e.S <- coxph(Surv(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS,x = TRUE)
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit")) ## dtS$X1
## automatic calculation
e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, short = 2))
e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1[,.SD,.SDcols = names(dt.ate2)], dt.ate2, tol = 1e-8)
## manual calculation
dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.Surv <- predictCox(e.S, newdata = iDT, times = tau, type = "survival", iid = TRUE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(1-iPred.Surv$survival)]
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(-iPred.Surv$survival.iid[,1,])]
iDT[, iid.IPTW := (X1test*Y/pi - mean(X1test*Y/pi))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y/pi^2))]
iDT[, iid.AIPTW := (X1test*Y/pi + r*(1-X1test/pi) - mean(X1test*Y/pi + r*(1-X1test/pi)))/.N]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*(Y-r)/pi^2))]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(-iPred.Surv$survival.iid[,1,], scale = 1-X1test/pi))]
return(iDT)
}))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.43356267, 0.78109942),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y/pi),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.44100313, 0.74666667),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*(event==1)*(time<=tau)/pi + r*(1-X1test/pi)),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.44729274, 0.76956544),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
unname(dtCf$iid.Gformula))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
unname(dtCf$iid.IPTW))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
unname(dtCf$iid.AIPTW))
## check standard error
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.Gformula^2),by="X1f"][[2]]),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07227568, 0.09615608),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.IPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07837068, 0.12110765),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.AIPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07701387, 0.10447564),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.3475368, 0.3056635, 0.3222727),
"se" = c(0.1028439, 0.1420700, 0.1109526),
"lower" = c(0.14596632, 0.02721154, 0.10480955),
"upper" = c(0.5491072, 0.5841155, 0.5397359),
"p.value" = c(0.00072680, 0.03143674, 0.00367726),
"quantileBand" = c(1.962359, 1.973331, 1.915003),
"adj.p.value" = c(0.0007, 0.0296, 0.0056),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(1.801584, 1.693110, 1.720496),
"se" = c(0.3194896, 0.4006286, 0.3214094),
"lower" = c(1.175396, 0.907892, 1.090545),
"upper" = c(2.427772, 2.478327, 2.350447),
"p.value" = c(0.01210904, 0.08362040, 0.02498223),
"quantileBand" = c(1.971454, 1.969537, 1.952972),
"adj.p.value" = c(0.0118, 0.0832, 0.0246),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
GS1 <- dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))]
expect_equal(ignore_attr=TRUE,GS, GS1, tol = 1e-6)
## compare to ipw package
if (requireNamespace("ipw",quietly=TRUE)){
ww <- ipw::ipwpoint(exposure=X1,family="binomial",link="logit",
denominator=~X2,
data=dtS)$ipw.weights
expect_equal(ignore_attr=TRUE,ww,
dtCf[,1/pi[X1test==1], by = "time"][[2]])
}
})
## ** Censoring - manual computation
n <- 5e1
set.seed(10)
dtS <- sampleData(n,outcome="survival")
tau <- median(dtS$time)
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
dtS$C <- (dtS$time<=tau)*(dtS$event!=0)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
test_that("[ate] Censoring, survival - check vs. manual calculations", {
e.S <- coxph(Surv(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, x = TRUE , y = TRUE)
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit"))
e.C <- coxph(Surv(time, event == 0) ~ X1f + X2, data = dtS, x = TRUE, y = TRUE)
## automatic calculation
e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, p.value = TRUE, short = 2))
e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE
)
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, p.value = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1[,.SD,.SDcols = names(dt.ate2)], dt.ate2, tol = 1e-8)
## ## manual calculation ## ##
iPred.Cens <- predictCox(e.C, newdata = dtS, times = pmin(tau,dtS$time-1e-10), type = "survival", iid = TRUE)
iPred.Cens$survivalDiag <- diag(iPred.Cens$survival)
iPred.Cens$survivalDiag.iid <- sapply(1:n, function(iObs){iPred.Cens$survival.iid[,iObs,iObs]})
## augmentation term
jumpC <- sort(e.C$y[e.C$y[,"status"]==1,"time"])
indexJump <- prodlim::sindex(jump.time = jumpC, eval.times = pmin(tau,dtS$time))
pred.Surv_tau <- predictCox(e.S, newdata = dtS, times = tau, type = "survival", iid = TRUE)
pred.Surv_jump <- predictCox(e.S, newdata = dtS, times = jumpC, type = "survival", iid = TRUE)
pred.Surv_beforejump <- predictCox(e.S, newdata = dtS, times = jumpC-1e-6, type = "survival", iid = TRUE)
pred.G_jump <- predictCox(e.C, newdata = dtS, times = jumpC-1e-10, type = "survival", iid = TRUE)
pred.dN_jump <- do.call(cbind,lapply(jumpC, function(iJ){iJ == dtS$time}))
pred.dLambda_jump <- predictCox(e.C, newdata = dtS, times = jumpC, type = "hazard", iid = TRUE)
pred.dM_jump <- pred.dN_jump-pred.dLambda_jump$hazard
term.SS <- colCenter_cpp(pred.Surv_jump$survival,pred.Surv_tau$survival)/pred.Surv_beforejump$survival
integrand_jump <- term.SS*pred.dM_jump/pred.G_jump$survival
augTerm <- sapply(1:n, function(iObs){
if(indexJump[iObs]==0){
return(0)
}else{
return(sum(integrand_jump[iObs,1:indexJump[iObs]]))
}
})
augTermY1.iid <- calcIterm(factor = pred.dM_jump/(pred.Surv_beforejump$survival*pred.G_jump$survival),
iid = -pred.Surv_tau$survival.iid,
indexJump = indexJump,
iid.outsideI = TRUE)
augTermY2.iid <- calcIterm(factor = -pred.dM_jump/(pred.Surv_beforejump$survival*pred.G_jump$survival),
iid = -pred.Surv_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermSurv.iid <- calcIterm(factor = - term.SS * pred.dM_jump/(pred.Surv_beforejump$survival * pred.G_jump$survival),
iid = pred.Surv_beforejump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG1.iid <- calcIterm(factor = - term.SS * pred.dM_jump/pred.G_jump$survival^2,
iid = pred.G_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG2.iid <- calcIterm(factor = - term.SS / pred.G_jump$survival,
iid = pred.dLambda_jump$hazard.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,C,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.Surv_tau <- predictCox(e.S, newdata = iDT, times = tau, type = "survival", iid = TRUE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(1-iPred.Surv_tau$survival)]
iDT[, G := as.double(iPred.Cens$survivalDiag)]
iDT[, I := augTerm]
## Gformula ##
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(-iPred.Surv_tau$survival.iid[,1,])]
## IPTW
iDT[, iid.IPTW := (X1test*Y*C/(pi*G) - mean(X1test*Y*C/(pi*G)))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y*C/(pi^2*G)))]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
## AIPTW,AIPCW ##
iDT[, iid.AIPTW.ate := (X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi - mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi))/.N]
## outcome
iDT[, iid.AIPTW.outcome := rowMeans(rowMultiply_cpp(-iPred.Surv_tau$survival.iid[,1,], (1-X1test/pi)))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY1.iid,X1test/pi))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY2.iid,X1test/pi))]
## treatment
iDT[, iid.AIPTW.treatment := rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -(X1test/pi^2)*(Y*C/G - r + I)))]
## survival
iDT[, iid.AIPTW.survival := rowMeans(rowMultiply_cpp(augTermSurv.iid, X1test/pi))]
## censoring
iDT[, iid.AIPTW.censoring := rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG1.iid,X1test/pi))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG2.iid,X1test/pi))]
## total
iDT[, iid.AIPTW := iid.AIPTW.ate + iid.AIPTW.outcome + iid.AIPTW.treatment + iid.AIPTW.survival + iid.AIPTW.censoring]
return(iDT)
}))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.38160373, 0.69088127),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G)),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.37314136, 0.75714469),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.37667566, 0.77294549),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
dtCf$iid.Gformula)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
dtCf$iid.IPTW)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
dtCf$iid.AIPTW)
## check standard errors
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.Gformula^2)), by = "X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.069586, 0.1201558),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.IPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07693792, 0.12423590),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.AIPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.07484163, 0.10880191),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.3092775, 0.3840033, 0.3962698),
"se" = c(0.1227820, 0.1425698, 0.1121352),
"lower" = c(0.0686292, 0.1045717, 0.1764890),
"upper" = c(0.5499259, 0.6634350, 0.6160507),
"p.value" = c(0.01177169, 0.00707186, 0.00040954),
"quantileBand" = c(1.965073, 1.948196, 1.931335),
"adj.p.value" = c(0.0122, 0.0079, 0.0007),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(1.810468, 2.029110, 2.052019),
"se" = c(0.3948003, 0.5204967, 0.4234462),
"lower" = c(1.036673, 1.008955, 1.222079),
"upper" = c(2.584262, 3.049264, 2.881958),
"p.value" = c(0.04008663, 0.04802263, 0.01297622),
"quantileBand" = c(1.961192, 1.959446, 1.952883),
"adj.p.value" = c(0.0392, 0.0459, 0.0129),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS,dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
})
## * [ate] IPCW logistic regression
## ** no censoring
set.seed(10)
n <- 500
tau <- 1:5
d <- sampleData(n, outcome = "competing.risks")
d$Y <- (d$event == 1)*(d$time <= tau[3])
d0 <- d[event!=0] ## remove censoring
d0.0 <- copy(d0)[,X1 := factor(0,levels = levels(X1))]
d0.1 <- copy(d0)[,X1 := factor(1,levels = levels(X1))]
e.T <- glm(X1~X2+X6,family="binomial",data=d0)
myW.T0 <- (d0$X1=="0")/(1-predict(e.T, type = "response"))
myW.T1 <- (d0$X1=="1")/predict(e.T, type = "response")
test_that("[ate] IPCW LR - no censoring", {
e0.wglm <- wglm(regressor.event = ~ X1+X2+X6, formula.censor = Surv(time,event==0) ~ 1,
times = tau, data = d0)
e0.glm <- glm(Y ~ X1+X2+X6, family = binomial, data = d0)
## NOTE difference between wglm and glm is that lava:::information.glm uses numerical derivatives
## while information.wglm uses explicit formula
## this lead to small difference in the influence function and therefore in the standard error
eATE.wglm <- ate(e0.wglm, data = d0, treatment = "X1", times = tau, band = FALSE, verbose = FALSE)
eATE.glm <- ate(e0.glm, data = d0, treatment = "X1", times = tau[3], verbose = FALSE)
## e0.mets <- logitATE(Y~X1+X2+X6, treat.model=X1~X2+X6, data = d)
## eATE.boot <- ate(e0.glm, data = d0, treatment = "X1", times = tau[3], verbose = FALSE, B = 1000)
## confint(eATE.boot)$meanRisk$se ## 0.02535254 0.06612763
## confint(eATE.boot)$diffRisk$se ## 0.06984537
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$estimate, eATE.wglm$meanRisk[time==tau[3],estimate], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$se, eATE.wglm$meanRisk[time==tau[3],se], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$lower, eATE.wglm$meanRisk[time==tau[3],lower], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE.glm$meanRisk$upper, eATE.wglm$meanRisk[time==tau[3],upper], tol = 1e-4)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e0.glm, newdata = d0.0)), eATE.glm$meanRisk$estimate[1], tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e0.glm, newdata = d0.1)), eATE.glm$meanRisk$estimate[2], tol = 1e-5)
## eATE.mets <- logitATE(Event(time,event)~X1+X2+X6, data = d, cause = 1, time = tau[3], treat.model = X1~1)
## coef(eATE.mets)
## coef(e0.glm)
## not the same!!
## coef(e0.wglm)
eATE2.wglm <- ate(e0.wglm, data = d0, treatment = X1~X2+X6, times = tau, band = FALSE, verbose = FALSE)
eATE2.glm <- ate(e0.glm, data = d0, treatment = X1~X2+X6, times = tau[3], verbose = FALSE)
## e.mets <- logitATE(Y~X1+X2+X6, treat.model=X1~X2, data = d)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$estimate, eATE2.wglm$meanRisk[time==tau[3],estimate], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$se, eATE2.wglm$meanRisk[time==tau[3],se], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$lower, eATE2.wglm$meanRisk[time==tau[3],lower], tol = 1e-4)
expect_equal(ignore_attr=TRUE,eATE2.glm$meanRisk$upper, eATE2.wglm$meanRisk[time==tau[3],upper], tol = 1e-4)
## A (Y-f_a) / pi + f_a = AY/pi - (A/pi-1) f_a
expect_equal(ignore_attr=TRUE,mean( myW.T0 * d0$Y - (myW.T0-1) * predictRisk(e0.glm, newdata = d0.0)), eATE2.glm$meanRisk$estimate[1], tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean( myW.T1 * d0$Y - (myW.T1-1) * predictRisk(e0.glm, newdata = d0.1)), eATE2.glm$meanRisk$estimate[2], tol = 1e-5)
})
## ** censoring
## *** data
d <- data.frame("Ytrue" = c(0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
"status" = c(2, 2, 0, 1, 1, 0, 1, 0, 0, 2, 2, 0, 0, 1, 0, 1, 2, 0, 2, 1, 2, 1, 2, 0, 1, 1, 0, 1, 2, 0, 1, 2, 1, 0, 0, 1, 2, 1, 1, 0, 1, 2, 2, 1, 0, 1, 0, 0, 2, 0, 1, 1, 0, 0, 1, 0, 1, 1, 2, 0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1, 0, 1, 1, 1, 1, 2, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 1, 1, 1, 1, 2, 1, 1, 1, 0, 2, 2, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 2, 0, 0, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 0, 1, 1, 2, 0, 2, 2, 1, 0, 1, 2, 0, 1, 2, 0, 2, 2, 0, 2, 0, 1, 0, 1, 0, 1, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1, 1, 0, 2, 2, 1, 1, 1, 1, 2, 0, 1, 0, 2, 1, 0, 0, 2, 2, 2, 1, 2, 0, 1, 2, 1, 1, 2, 0, 0, 2, 1, 2, 0, 0, 2, 2, 2, 1, 1, 0, 0, 0, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1),
"time" = c(0.1029, 0.133, 0.1403, 0.1659, 0.171, 0.1868, 0.1973, 0.2055, 0.2083, 0.2182, 0.2598, 0.3054, 0.3277, 0.3295, 0.3558, 0.3581, 0.3697, 0.3952, 0.4796, 0.4884, 0.5139, 0.6098, 0.619, 0.6255, 0.6374, 0.6517, 0.6687, 0.6856, 0.7404, 0.7557, 0.7625, 0.7757, 0.831, 0.8844, 0.9163, 0.9347, 0.9645, 1.0534, 1.078, 1.0848, 1.1464, 1.1603, 1.1778, 1.179, 1.1942, 1.1948, 1.2169, 1.2453, 1.2701, 1.2899, 1.3211, 1.3882, 1.413, 1.4392, 1.4407, 1.4407, 1.441, 1.451, 1.5042, 1.5496, 1.5595, 1.5974, 1.8156, 1.8209, 1.8228, 1.8495, 1.8504, 1.8515, 1.8887, 1.9146, 1.915, 2.0289, 2.0512, 2.0874, 2.1309, 2.1318, 2.1405, 2.1658, 2.1674, 2.1797, 2.1944, 2.2099, 2.2665, 2.2956, 2.4062, 2.444, 2.4472, 2.4835, 2.5011, 2.5401, 2.5416, 2.5741, 2.5825, 2.6255, 2.6335, 2.6452, 2.649, 2.6695, 2.7385, 2.7989, 2.8758, 2.8894, 2.9074, 2.9453, 2.996, 3.0597, 3.0712, 3.0835, 3.0847, 3.1286, 3.1626, 3.163, 3.1785, 3.182, 3.2286, 3.3285, 3.3329, 3.4164, 3.4316, 3.5536, 3.5564, 3.5627, 3.6225, 3.6551, 3.668, 3.7024, 3.7223, 3.8078, 3.808, 3.8125, 3.8339, 3.8605, 3.8658, 3.8713, 3.8919, 3.9118, 3.9612, 3.9998, 4.0246, 4.0548, 4.0558, 4.1904, 4.2288, 4.3213, 4.3842, 4.4425, 4.5236, 4.5913, 4.6208, 4.6848, 4.6911, 4.6916, 4.7735, 4.7874, 4.794, 4.8415, 4.8639, 4.871, 4.9216, 4.9305, 4.932, 4.9491, 5.0137, 5.0658, 5.066, 5.0807, 5.271, 5.3019, 5.3891, 5.3942, 5.4995, 5.7012, 5.7433, 5.7482, 5.7803, 5.7886, 5.8666, 5.868, 6.025, 6.1046, 6.1366, 6.2138, 6.2556, 6.2578, 6.2948, 6.3285, 6.4076, 6.4339, 6.6006, 6.6189, 6.6641, 6.7867, 6.822, 6.8455, 7.0006, 7.1658, 7.2079, 7.208, 7.2602, 7.2829, 7.5101, 7.5113, 7.57, 7.6057, 7.6087, 7.7833, 7.9494, 7.9787, 8.0451, 8.0859, 8.1161, 8.1361, 8.1395, 8.3796, 8.4503, 8.5977, 8.6304, 8.6412, 8.771, 8.9343, 8.9413, 8.979, 9.0034, 9.0179, 9.1283, 9.2646, 9.2819, 9.46, 9.6075, 10.0297, 10.4142, 10.4334, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11),
"A" = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1),
"X" = c(0.591, -0.7045, 0.1998, -1.2, -0.2748, 0.6488, -0.0464, 0.0709, -1.2, -0.1084, 0.1463, 1.2, 0.7424, 1.2, 1.1246, -0.2878, 0.6152, -0.42, 1.2, 1.2, 0.3618, 1.1865, 0.0818, -1.2, 0.8593, -0.1021, 1.1739, 0.2807, -0.8726, 1.0116, 0.4228, 0.6142, -0.3044, -0.7147, -0.7772, -0.5083, -0.3825, 0.7667, 1.2, 0.5253, 1.2, 1.2, -0.4935, -1.2, 0.7212, -0.9438, 0.2387, -1.2, 0.5291, -0.1674, 1.1412, -0.9328, 0.6634, 0.3333, -0.7123, 1.2, -0.4832, 0.3565, 0.5722, -1.0329, -0.2241, -0.8411, 1.2, 0.7209, 0.6254, 0.1107, -0.2716, 0.0051, -1.0889, 1.2, 1.2, -0.423, 1.2, 0.4794, -0.4668, 0.2772, -0.5024, -0.3908, -0.1373, 0.6094, 0.1762, 0.8822, 0.1645, -0.6743, -0.5285, -0.3291, 1.0172, 0.0233, -0.4747, 1.2, -1.2, 0.0924, -1.2, 0.3483, -0.386, 0.5705, 1.2, 1.1889, -0.1459, 1.1521, 1.2, 0.2255, 0.3866, 0.1753, -1.2, 1.2, -0.6759, 0.6161, -0.0756, -0.1158, -0.0654, 1.2, -0.0816, -0.8717, 0.3017, -0.982, -0.4491, 0.1316, 1.05, -0.9845, 0.3785, -0.8026, 0.2651, -0.9233, 1.2, -0.1918, 1.2, -0.7468, 0.7581, -0.3449, -1.2, -0.079, 0.0295, -0.4644, 0.1143, 0.8306, 0.0565, 1.2, -0.5819, 1.2, 0.572, -0.5393, -0.1491, 0.4831, 0.0408, -0.9452, 0.6162, 0.8923, 1.2, -0.0585, 0.0334, -0.6505, -1.0751, 0.6102, 0.717, -0.6497, -1.2, -0.3648, 0.4697, -1.0421, 0.1834, -1.2, -0.7025, -0.1347, -0.785, -0.8318, -0.7814, -0.2372, -0.2614, -1.0397, -1.2, 0.6774, -0.8028, -1.0292, -1.2, 0.0708, -0.0817, -0.4429, 0.413, -0.1401, -0.8748, -1.2, -0.7557, -1.0771, -0.838, -0.446, -0.8366, -0.526, -0.5437, -1.2, -0.6688, 0.4963, -1.2, -0.495, 0.779, -1.2, -0.7388, -0.6316, -0.6253, -0.564, 0.5762, 1.1168, 0.0583, 0.2203, -0.1059, 0.6471, -0.0811, 0.6312, 0.0156, -0.8045, -1.1241, 0.4943, -0.2819, -0.1622, 0.1603, -1.2, 0.5105, 0.4579, -0.7416, 0.8165, -1.193, -0.5748, -0.424, -1.0011, -1.2, -1.0157, 0.688, -0.8993, -0.2307, -0.0179, -0.1043, -0.8387, 0.2837, 0.1785, -0.6486, -1.2, -0.399, -0.8616, -1.2, -0.4967, -1.2, -0.7369, -5e-04, 0.1076, -1.0822, -0.2976, 0.617, -1.2, -0.428, -1.2))
d$A2 <- as.factor(d$A)
d$X2 <- d$X^2
d$Y1 <- as.numeric(d$time<=5)*as.numeric(d$status==1)
## *** prepare
## create weights for AIPTW
e.T <- glm(A2~X,family="binomial",data=d)
myW.T1 <- (d$A2=="1")/predict(e.T, type = "response")
myW.T0 <- (d$A2=="0")/(1-predict(e.T, type = "response"))
## range(1/d$PS2 - myW.T)
## range((iW.IPTW[,2]-iW.IPTW[,1]) - myW.T)
e.C <- coxph(Surv(time,status==0)~1,data=d, x = TRUE)
myW.C <- (1-(d$time < 5)*(d$status==0))/predictCoxPL(e.C, newdata = d, times = pmin(d$time,5), diag = TRUE)$survival
## range(iW.IPCW - myW.C)
## range(d[["IPCW"]] - myW.C)
## create counterfactual data
d1 <- copy(d)
d1$A <- 1
d1$A2 <- factor(1,levels = 0:1)
d0 <- copy(d)
d0$A <- 0
d0$A2 <- factor(0,levels = 0:1)
## *** test
test_that("[ate] IPCW LR - censoring (based on Paul's script and results)", {
## no censoring: G-formula on glm
e.glm <- glm(Ytrue~X+A2,family="binomial",data=d)
eATE.wglm <- ate(e.glm, data = d, treatment = "A2", band = FALSE, verbose = FALSE)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e.glm, newdata = d0)),
0.2468305, tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean(predictRisk(e.glm, newdata = d1)),
0.4593332, tol = 1e-5)
expect_equal(ignore_attr=TRUE,eATE.wglm$meanRisk$estimate,
c(0.2468305,0.4593332), tol = 1e-5)
## censoring:
## IPCW model
suppressWarnings(eW.glm <- glm(Y1~X2+A2,family="binomial",data=d, weights = myW.C))
e.wglm <- wglm(regressor.event = ~ X2+A2,
formula.censor = Surv(time,status==0) ~ 1,
times = 5, data = d, product.limit = TRUE)
## e.mets <- logitIPCW(Event(time,status)~X2+A2, data = d, cause = 1, time = 5, cens.model=~1)
## expect_equal(ignore_attr=TRUE,as.double(coef(e.mets)), as.double(coef(e.wglm)), tol = 1e-3)
## G-formula on IPCW glm
eATE.wglm <- ate(e.wglm, data = d, treatment = "A2", times = 5, band = FALSE, verbose = FALSE)
expect_equal(ignore_attr=TRUE,mean(predictRisk(eW.glm, newdata = d0)), 0.1886021, tol = 1e-5)
expect_equal(ignore_attr=TRUE,mean(predictRisk(eW.glm, newdata = d1)), 0.4925792, tol = 1e-5)
## minor difference due to coxph using Newton Raphson instead of explicit formulae
expect_equal(ignore_attr=TRUE,eATE.wglm$meanRisk$estimate,
c(0.1886021,0.4925792), tol = 1e-3)
## minor difference due to difference weights computations
## eATE.mets <- logitIPCWATE(Event(time,status)~X2+A2, data = d, cause = 1, time = 5, treat.model = A2~1, cens.model=~1)
## expect_equal(ignore_attr=TRUE,as.double(coef(eATE.mets)), as.double(coef(e.wglm)), tol = 1e-3)
## expect_equal(ignore_attr=TRUE,summary(eATE.mets)$ateG[2:1,"Estimate"],unlist(confint(eATE.wglm)$meanRisk[,"estimate"]), tol = 1e-4)
## expect_equal(ignore_attr=TRUE,summary(eATE.mets)$ateG[2:1,"Std.Err"],unlist(confint(eATE.wglm)$meanRisk[,"se"]), tol = 1e-2)
## some difference in standard error
## AIPTW on IPCW glm
eATE2.wglm <- ate(e.wglm, treatment = A2 ~ X, censor = Surv(time,status==0) ~ 1,
product.limit = TRUE, estimator = c("Gformula","AIPTW"),
data = d, times = 5, band = FALSE, verbose = FALSE)
## eATE2.wglm$diffRisk
## estimator time A estimate.A B estimate.B estimate se lower
## 1: GFORMULA 5 0 0.1886021 1 0.4925629 0.3039607 0.06488501 0.17678846
## 2: AIPTW 5 0 0.2460310 1 0.4591394 0.2131084 0.07828391 0.05967479
## upper p.value
## 1: 0.4311330 2.804980e-06
## 2: 0.3665421 6.483893e-03
expect_equal(ignore_attr=TRUE,eATE2.wglm$meanRisk[estimator == "AIPTW",estimate],
c(mean( myW.T0*myW.C*d$Y1 - (myW.T0 - 1) * predict(eW.glm, newdata = d0, type = "response") ),
mean( myW.T1*myW.C*d$Y1 - (myW.T1 - 1) * predict(eW.glm, newdata = d1, type = "response") )),
tol = 1e-4)
## minor difference due to difference weights computations
## eATE2.mets <- logitIPCWATE(Event(time,status)~X2+A2, data = d, cause = 1, time = 5, treat.model = A2~X, cens.model=~1)
## expect_equal(ignore_attr=TRUE,as.double(coef(eATE2.mets)), as.double(coef(e.wglm)), tol = 1e-3)
## expect_equal(ignore_attr=TRUE,summary(eATE2.mets)$ateG[2:1,"Estimate"],unlist(confint(eATE2.wglm)$meanRisk[4:3,"estimate"]), tol = 1e-4)
## expect_equal(ignore_attr=TRUE,summary(eATE.mets)$ateG[2:1,"Std.Err"],unlist(confint(eATE.wglm)$meanRisk[,"se"]), tol = 1e-2)
## big difference in estimate and se. Not sure why.
})
## * [ate] Competing risk case
cat("[ate] Competing risk case \n")
## ** no censoring - manual computation
## *** Data
n <- 1e2
tau <- 1.5
set.seed(10)
dtS <- sampleData(n, outcome="competing.risks")[event != 0]
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
## *** check
test_that("[ate] no censoring, competing risks - check vs. manual calculations", {
e.S <- CSC(Hist(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, surv.type = "survival")
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit")) ## dtS$X1
## automatic calculation
suppressWarnings(e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE, cause = 1,
band = FALSE, product.limit = FALSE
))
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, p.value = TRUE, short = 2))
suppressWarnings(e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE, cause = 1,
iid = TRUE, product.limit = FALSE
))
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, p.value = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1[,.SD,.SDcols = names(dt.ate2)], dt.ate2)
## manual calculation
suppressWarnings(dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.risk <- predict(e.S, newdata = iDT, times = tau, iid = TRUE,
cause = 1, product.limit = FALSE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(iPred.risk$absRisk)]
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(iPred.risk$absRisk.iid[,1,])]
iDT[, iid.IPTW := (X1test*Y/pi - mean(X1test*Y/pi))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y/pi^2))]
iDT[, iid.AIPTW := (X1test*Y/pi + r*(1-X1test/pi) - mean(X1test*Y/pi + r*(1-X1test/pi)))/.N]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*(Y-r)/pi^2))]
iDT[, iid.AIPTW := iid.AIPTW + rowMeans(rowMultiply_cpp(iPred.risk$absRisk.iid[,1,], scale = 1-X1test/pi))]
return(iDT)
})))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.09315393, 0.37259431),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y/pi),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.08405157, 0.26793249),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*(event==1)*(time<=tau)/pi + r*(1-X1test/pi)),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.07847289, 0.47497984),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
unname(dtCf$iid.Gformula))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
unname(dtCf$iid.IPTW))
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
unname(dtCf$iid.AIPTW))
## check standard error
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.Gformula^2),by="X1f"][[2]]),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.0315163, 0.2407176),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.IPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.03282852, 0.15183527),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,sqrt(dtCf[,sum(iid.AIPTW^2),by="X1f"][[2]]),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.03109236, 0.27245093),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.2794404, 0.1838809, 0.3965070),
"se" = c(0.2272017, 0.1549114, 0.2688568),
"lower" = c(-0.1658668, -0.1197399, -0.1304426),
"upper" = c(0.7247475, 0.4875017, 0.9234565),
"p.value" = c(0.2187263, 0.2352249, 0.1402693),
"quantileBand" = c(1.969141, 1.922815, 1.933780),
"adj.p.value" = c(0.2235, 0.2291, 0.1376),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(3.999770, 3.187715, 6.052789),
"se" = c(2.266232, 2.180114, 3.865633),
"lower" = c(0, 0, 0),
"upper" = c( 8.441504, 7.460660, 13.629289),
"p.value" = c(0.1856088, 0.3156261, 0.1911770),
"quantileBand" = c(1.971403, 1.958227, 1.974396),
"adj.p.value" = c(0.1872, 0.3072, 0.1973),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
})
## ** Censoring - manual computation, surv.type="survival"
n <- 1e2
set.seed(10)
dtS <- sampleData(n, outcome="competing.risks")
tau <- median(dtS$time)
dtS$Y <- (dtS$time<=tau)*(dtS$event==1)
dtS$C <- (dtS$time<=tau)*(dtS$event!=0)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
## GIVES WARNING
## Estimated risk outside the range [0,1].
## Possible cause: incorrect extrapolation, i.e., time and/or covariates used for the prediction differ from those used to fit the Cox models.
test_that("[ate] Censoring, competing risks (surv.type=\"survival\") - check vs. manual calculations", {
e.S <- CSC(Hist(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, surv.type = "survival")
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit"))
e.C <- coxph(Surv(time, event == 0) ~ X1f + X2, data = dtS, x = TRUE, y = TRUE)
## automatic calculation
suppressWarnings(e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE, cause = 1
))
set.seed(15)
dt.ate1 <- as.data.table(summary(e.ate1, band = TRUE, p.value = TRUE, short = 2))
suppressWarnings(e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
censor = e.C,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
band = FALSE, product.limit = FALSE, cause = 1
))
set.seed(15)
dt.ate2 <- as.data.table(summary(e.ate2, band = TRUE, p.value = TRUE, short = 2))
expect_equal(ignore_attr=TRUE,dt.ate1, dt.ate2, tol = 1e-8)
## ## manual calculation ## ##
iPred.Cens <- predictCox(e.C, newdata = dtS, times = pmin(tau,dtS$time-1e-10), type = "survival", iid = TRUE)
iPred.Cens$survivalDiag <- diag(iPred.Cens$survival)
iPred.Cens$survivalDiag.iid <- sapply(1:n, function(iObs){iPred.Cens$survival.iid[,iObs,iObs]})
## augmentation term
jumpC <- sort(e.C$y[e.C$y[,"status"]==1,"time"])
indexJump <- prodlim::sindex(jump.time = jumpC, eval.times = pmin(tau,dtS$time))
pred.Risk_tau <- predict(e.S, newdata = dtS, times = tau, iid = TRUE, cause = 1, product.limit = FALSE)
pred.Risk_jump <- predict(e.S, newdata = dtS, times = jumpC, iid = TRUE, cause = 1, product.limit = FALSE)
pred.Surv_jump <- predictCox(e.S$models[["OverallSurvival"]], newdata = dtS, times = jumpC-1e-6, type = "survival", iid = TRUE)
pred.G_jump <- predictCox(e.C, newdata = dtS, times = jumpC-1e-10, type = "survival", iid = TRUE)
pred.dN_jump <- do.call(cbind,lapply(jumpC, function(iJ){(iJ == dtS$time)*(dtS$event==0)}))
pred.dLambda_jump <- predictCox(e.C, newdata = dtS, times = jumpC, type = "hazard", iid = TRUE)
pred.dM_jump <- pred.dN_jump-pred.dLambda_jump$hazard
term.SS <- -colCenter_cpp(pred.Risk_jump$absRisk,pred.Risk_tau$absRisk)/pred.Surv_jump$survival
integrand_jump <- term.SS*pred.dM_jump/pred.G_jump$survival
augTerm <- sapply(1:n, function(iObs){
if(indexJump[iObs]==0){
return(0)
}else{
return(sum(integrand_jump[iObs,1:indexJump[iObs]]))
}
})
augTermY1.iid <- calcIterm(factor = pred.dM_jump/(pred.Surv_jump$survival*pred.G_jump$survival),
iid = pred.Risk_tau$absRisk.iid,
indexJump = indexJump,
iid.outsideI = TRUE)
augTermY2.iid <- calcIterm(factor = -pred.dM_jump/(pred.G_jump$survival*pred.Surv_jump$survival),
iid = pred.Risk_jump$absRisk.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermSurv.iid <- calcIterm(factor = - term.SS * pred.dM_jump/(pred.Surv_jump$survival * pred.G_jump$survival),
iid = pred.Surv_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG1.iid <- calcIterm(factor = - term.SS * pred.dM_jump/pred.G_jump$survival^2,
iid = pred.G_jump$survival.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
augTermG2.iid <- calcIterm(factor = - term.SS / pred.G_jump$survival,
iid = pred.dLambda_jump$hazard.iid,
indexJump = indexJump,
iid.outsideI = FALSE)
dtCf <- do.call(rbind,lapply(levels(dtS$X1f), function(iT){ ## iT <- "0"
iDT <- data.table::copy(dtS[,.(event,time,Y,C,X1,X2,X3,X6)])
iDT[, X1f := factor(iT,levels(dtS$X1f))]
iDT[, X1test := iT==as.character(X1)]
iPred.logit <- predictRisk(e.T, newdata = iDT, iid = TRUE, level = iT)
iPred.risk_tau <- predict(e.S, newdata = iDT, times = tau, iid = TRUE, cause = 1, product.limit = FALSE)
iDT[, pi := as.double(iPred.logit)]
iDT[, r := as.double(iPred.risk_tau$absRisk)]
iDT[, G := as.double(iPred.Cens$survivalDiag)]
iDT[, I := augTerm]
## Gformula ##
iDT[, iid.Gformula := (r - mean(r))/.N]
iDT[, iid.Gformula := iid.Gformula + rowMeans(iPred.risk_tau$absRisk.iid[,1,])]
## IPTW
iDT[, iid.IPTW := (X1test*Y*C/(pi*G) - mean(X1test*Y*C/(pi*G)))/.N]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -X1test*Y*C/(pi^2*G)))]
iDT[, iid.IPTW := iid.IPTW + rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
## AIPTW,AIPCW ##
iDT[, iid.AIPTW.ate := (X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi - mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi))/.N]
## outcome
iDT[, iid.AIPTW.outcome := rowMeans(rowMultiply_cpp(iPred.risk_tau$absRisk.iid[,1,], (1-X1test/pi)))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY1.iid,X1test/pi))]
iDT[, iid.AIPTW.outcome := iid.AIPTW.outcome + rowMeans(rowMultiply_cpp(augTermY2.iid,X1test/pi))]
## treatment
iDT[, iid.AIPTW.treatment := rowMeans(rowMultiply_cpp(attr(iPred.logit,"iid"), scale = -(X1test/pi^2)*(Y*C/G - r + I)))]
## survival
iDT[, iid.AIPTW.survival := rowMeans(rowMultiply_cpp(augTermSurv.iid, X1test/pi))]
## censoring
iDT[, iid.AIPTW.censoring := rowMeans(rowMultiply_cpp(iPred.Cens$survivalDiag.iid, scale = -X1test*Y*C/(pi*G^2)))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG1.iid,X1test/pi))]
iDT[, iid.AIPTW.censoring := iid.AIPTW.censoring + rowMeans(rowMultiply_cpp(augTermG2.iid,X1test/pi))]
## total
iDT[, iid.AIPTW := iid.AIPTW.ate + iid.AIPTW.outcome + iid.AIPTW.treatment + iid.AIPTW.survival + iid.AIPTW.censoring]
return(iDT)
}))
## check estimate
expect_equal(ignore_attr=TRUE,dtCf[,mean(r),by="X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.32111469, 0.99623763),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G)),by="X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.31871273, 0.49590715),
dt.ate1[estimator == "IPTW" & type == "meanRisk",estimate],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,mean(X1test*Y*C/(pi*G) + r*(1-X1test/pi) + I*X1test/pi),by="X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate])
expect_equal(ignore_attr=TRUE,c(0.29549588, 0.9961723),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",estimate],
tol = 1e-6)
## check influence function
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["GFORMULA"]])[,1]),
dtCf$iid.Gformula)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["IPTW"]])[,1]),
dtCf$iid.IPTW)
expect_equal(ignore_attr=TRUE,unname(do.call(rbind,e.ate1$iid[["AIPTW"]])[,1]),
dtCf$iid.AIPTW)
## check standard errors
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.Gformula^2)), by = "X1f"][[2]],
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.05007538, 0.42597332),
dt.ate1[estimator == "GFORMULA" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.IPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "IPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.04961228, 0.15883446),
dt.ate1[estimator == "IPTW" & type == "meanRisk",se],
tol = 1e-6)
expect_equal(ignore_attr=TRUE,dtCf[,sqrt(sum(iid.AIPTW^2)), by = "X1f"][[2]],
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se])
expect_equal(ignore_attr=TRUE,c(0.04803356, 0.316899),
dt.ate1[estimator == "AIPTW" & type == "meanRisk",se],
tol = 1e-6)
## check transformation
GS <- data.table("estimate" = c(0.6751229, 0.1771944, 0.7006764),
"se" = c(0.4352720, 0.1655279, 0.3273409),
"lower" = c(-0.17799448, -0.14723439, 0.05910013),
"upper" = c(1.0000000, 0.5016232, 1.0000000),
"p.value" = c(0.12089283, 0.28440314, 0.03231356),
"quantileBand" = c(1.958676, 1.963257, 1.952228),
"adj.p.value" = c(0.1177, 0.2880, 0.0331),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "diffRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
GS <- data.table("estimate" = c(3.102436, 1.555969, 3.371188),
"se" = c(1.4694536, 0.5500765, 1.2732042),
"lower" = c(0.2223595, 0.4778388, 0.8757540),
"upper" = c(5.982512, 2.634099, 5.866623),
"p.value" = c(0.15249898, 0.31215422, 0.06254973),
"quantileBand" = c(1.955519, 1.941080, 1.992390),
"adj.p.value" = c(0.1553, 0.3178, 0.0605),
"estimator" = c("GFORMULA", "IPTW", "AIPTW"))
expect_equal(ignore_attr=TRUE,GS, dt.ate1[type == "ratioRisk",.(estimate,se,lower,upper,p.value,quantileBand,adj.p.value,estimator=as.character(estimator))],
tol = 1e-6)
})
## ** Censoring - manual computation, surv.type="hazard"
n <- 1e2
set.seed(10)
dtS <- sampleData(n, outcome="competing.risks")
tau <- median(dtS$time)
dtS$X1f <- dtS$X1
dtS$X1 <- as.numeric(as.character(dtS$X1))
test_that("[ate] Censoring, competing risks (surv.type=\"hazard\") - check vs. manual calculations", {
e.S <- CSC(Hist(time, event) ~ X1f + strata(X2) + X3*X6, data = dtS, surv.type = "hazard")
e.T <- glm(X1f ~ X2, data = dtS, family = binomial(link = "logit"))
e.C <- coxph(Surv(time, event == 0) ~ X1f + X2, data = dtS, x = TRUE, y = TRUE)
## automatic calculation
suppressWarnings(e.ate1 <- ate(data = dtS, times = tau,
event = e.S,
treatment = e.T,
censor = e.C,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
iid = TRUE, product.limit = FALSE, cause = 1
))
dt.ate1 <- as.data.table(e.ate1)
suppressWarnings(e.ate2 <- ate(data = dtS, times = tau,
event = e.S,
censor = e.C,
treatment = e.T,
method.iid = 2,
estimator = c("Gformula","IPTW","AIPTW"),
verbose = FALSE,
iid = TRUE, product.limit = FALSE, cause = 1
))
dt.ate2 <- as.data.table(e.ate2)
expect_equal(ignore_attr=TRUE,dt.ate1, dt.ate2, tol = 1e-8)
})
## * [ate] Landmark analysis (time varying covariates)
cat("[ate] Landmark analysis \n")
fit <- coxph(Surv(time, status) ~ celltype+karno + age + trt, veteran)
vet2 <- survSplit(Surv(time, status) ~., veteran,
cut=c(60, 120), episode ="timegroup")
fitTD <- coxph(Surv(tstart, time, status) ~ celltype+karno + age + trt,
data= vet2,x=1)
test_that("[ate] landmark analyses", {
resVet <- ate(fitTD,formula=Hist(entry=tstart,time=time,event=status)~1,
data = vet2, treatment = "celltype", contrasts = NULL,
times=5,verbose=FALSE,
landmark = c(0,30,60,90), cause = 1, se = FALSE)
dt.resVet <- as.data.table(resVet)
dt.resVet$level <- factor(dt.resVet$level, levels = unique(dt.resVet$level))
setkeyv(dt.resVet,c("type","level"))
GS <- c(0.01773158, 0.02092285, 0.01489588, 0.02981096, 0.04098041, 0.04821725, 0.03463358, 0.06846231, 0.05589160, 0.06564467, 0.04741838, 0.09298832, 0.02633366, 0.03103968, 0.02217127, 0.04416996)
expect_equal(ignore_attr=TRUE,dt.resVet[type=="meanRisk",estimate], GS, tol = 1e-6)
GS <- c(0.02324883, 0.0272944, 0.01973769, 0.03865134, 0.03816002, 0.04472182, 0.0325225, 0.06317735, 0.00860208, 0.01011683, 0.00727539, 0.014359, 0.01491119, 0.01742742, 0.0127848, 0.02452601, -0.01464675, -0.01717757, -0.01246231, -0.02429234, -0.02955794, -0.03460499, -0.02524711, -0.04881836)
expect_equal(ignore_attr=TRUE,dt.resVet[type=="diffRisk",estimate], GS, tol = 1e-6)
GS <- c(2.31115372, 2.30452621, 2.32504339, 2.29654787, 3.15209352, 3.13746333, 3.18332093, 3.11926569, 1.48512762, 1.4835302, 1.488416, 1.48166834, 1.36386147, 1.3614353, 1.36914474, 1.3582411, 0.64259145, 0.64374629, 0.64016698, 0.64517198, 0.47115595, 0.47284384, 0.46756706, 0.47500549)
expect_equal(ignore_attr=TRUE,dt.resVet[type=="ratioRisk",estimate], GS, tol = 1e-6)
})
## * [ate] Case only
cat("[ate] Case only \n")
set.seed(11)
d <- sampleData(100, outcome = "survival")
d$id <- 1:NROW(d)
tau <- 3
## ** statistical model
e.cox <- coxph(Surv(time,event) ~ X1 + X2, x = TRUE, y = TRUE, data = d)
## ** ate: case only
test_that("Case only ate", {
## automatically
e.ateC <- ate(e.cox,
treatment = "X1",
data = d[X1 == "1"], time = tau, se = TRUE,
data.index = d[X1 == "1",id],
verbose = 0)
## manually
d0C <- copy(d[X1 == "1"])
d0C[, X1:= factor("0", levels = c("0","1"))]
d1C <- copy(d[X1 == "1"])
d1C[, X1:= factor("1", levels = c("0","1"))]
e.pred0C <- predictCox(e.cox, newdata = d0C, times = tau, average.iid = TRUE)
e.pred1C <- predictCox(e.cox, newdata = d1C, times = tau, average.iid = TRUE)
e2.ateC <- c(mean(1-e.pred0C$survival),
mean(1-e.pred1C$survival),
mean( (1-e.pred1C$survival)-(1-e.pred0C$survival)),
mean(1-e.pred1C$survival)/mean(1-e.pred0C$survival))
iid0C <- -e.pred0C$survival.average.iid
iid0C[d0C$id,] <- iid0C[d0C$id,] + (1-e.pred0C$survival-e2.ateC[1])/NROW(d0C)
iid1C <- -e.pred1C$survival.average.iid
iid1C[d1C$id,] <- iid1C[d1C$id,] + (1-e.pred1C$survival-e2.ateC[2])/NROW(d1C)
e2.se.ateC <- c(sqrt(sum(iid0C^2)),
sqrt(sum(iid1C^2)),
sqrt(sum((iid1C-iid0C)^2)),
sqrt(sum((iid1C/e2.ateC[1]-e2.ateC[2]*iid0C/e2.ateC[1]^2)^2))
)
expect_equal(ignore_attr=TRUE,as.data.table(e.ateC)$estimate, e2.ateC, tol = 1e-6)
expect_equal(ignore_attr=TRUE,as.data.table(e.ateC)$se, e2.se.ateC, tol = 1e-5)
})
## * [ate] Miscellaneous
cat("[ate] Miscellaneous \n")
## ** Pre-computation of iidCox does not affect the results
test_that("[ate] Cox model/G-formula - precompute iid", {
set.seed(10)
d <- sampleData(100)
e.CSC <- CSC(Hist(time,event)~X1+X2, data = d)
GS <- ate(e.CSC,
treatment = "X1", data = d, times = 1:5, cause = 1,
verbose = FALSE)
e.CSC <- iidCox(e.CSC)
test <- ate(e.CSC,
treatment = "X1", data = d, times = 1:5, cause = 1,
verbose = FALSE)
expect_equal(ignore_attr=TRUE,as.data.table(test), as.data.table(GS))
})
## ** Ate using list of formulae
## ** Specification of the censoring mecanism
test_that("[ate] Specification of the censoring mecanism",{
set.seed(10)
d <- sampleData(100)
d$allevent <- as.numeric(d$event>0)
expect_error(ate(event = Surv(time,allevent)~X1+X2,
censor = Surv(time,allevent)~X1,
treatment = X1 ~ X2,
data = d, times = 1:5, cause = 1,
verbose = FALSE))
expect_error(ate(event = Hist(time,event)~X1+X2,
censor = Surv(time,allevent)~X1,
treatment = X1 ~ X2,
data = d, times = 1:5, cause = 1,
verbose = FALSE))
## should run
test1 <- ate(event = Hist(time,event)~X1+X2,
censor = Surv(time,allevent==0)~X1,
treatment = X1 ~ X2,
data = d, times = 1:5, cause = 1,
verbose = FALSE)
})
## * [ate] Previous bug
cat("[ate] Previous bug \n")
## ** Results of ate depends on the number of timepoints
d <- sampleData(1000,outcome="survival")
test_that("[ate] Cox model/G-formula - one or several timepoints affects the results",{
fit <- coxph(Surv(time,event)~X1+X4+X7+X8,data=d,x=TRUE,y=TRUE)
a1 <- ate(fit,data=d,treatment="X1",time=5, verbose = FALSE)
a2 <- ate(fit,data=d,treatment="X1",time=5:7, verbose = FALSE)
expect_equal(ignore_attr=TRUE,a2$meanRisk[time==5,.(estimate,se)],
a1$meanRisk[time==5,.(estimate,se)])
expect_equal(ignore_attr=TRUE,a2$diffRisk[time==5,.(estimate,se)],
a1$diffRisk[,.(estimate,se)])
expect_equal(ignore_attr=TRUE,a2$ratioRisk[time==5,.(estimate,se)],
a1$ratioRisk[,.(estimate,se)])
})
## ** Before the first jump or at the first jump
set.seed(1)
d <- sampleData(100,outcome="survival")
tau <- c(d[event>0,min(time)] + c(-1e-5,+1e-5), median(d$time))
test_that("[ate] double robust estimator - before or at the first jump",{
## Error in rowSums(integrand.St[[iGrid]][, 1:beforeTau.nJumpC[iTau]]) :
## 'x' must be an array of at least two dimensions
fit <- ate(event = coxph(Surv(time,event)~X1+X4+X7+X8,data=d,x=TRUE,y=TRUE),
treatment = X1~X4,
censor = coxph(Surv(time,event==0)~X1+X4+X7+X8,data=d,x=TRUE,y=TRUE),
data=d, time=tau, se = TRUE, verbose = FALSE)
expect_output(print(fit))
})
## ** Multiple timepoints
set.seed(10)
n <- 1e2
dtS <- sampleData(n,outcome="competing.risks")
test_that("ate double robust estimator works with multiple timepoint",{
## TAG: Monday, Jul 8, 2019 9:09:36 PM (email subject Re: Branche ate for riskRegression ready)
e.CSC <- CSC(Hist(time, event) ~ X1 + X2 + X3,
data = dtS, surv.type = "hazard")
## previous error message
## Error in iidTotal[[contrasts[iC]]] + iid.treatment[[1]] : non-numeric argument to binary operator
e.ateRR <- ate(event = Hist(time,event) ~ X1 + X2 + X3,
treatment = glm(X1 ~ 1, data = dtS, family = binomial(link = "logit")),
censor = cph(Surv(time,event==0) ~ X1, data = dtS, x = TRUE, y = TRUE),
data = dtS, times = 3:4, verbose = FALSE, cause = 1
)
GS <- data.table("type" = c("meanRisk", "meanRisk", "meanRisk", "meanRisk", "diffRisk", "diffRisk", "ratioRisk", "ratioRisk"),
"estimator" = c("AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW", "AIPTW"),
"time" = c(3L, 3L, 4L, 4L, 3L, 4L, 3L, 4L),
"level" = factor(c("0", "1", "0", "1", "0.1", "0.1", "0.1", "0.1"), levels = c("0","1","0.1")),
"estimate" = c(0.2585028, 0.5034093, 0.3052605, 0.5005653, 0.2449065, 0.1953049, 1.9474034, 1.6397974),
"se" = c(0.04636428, 0.15684698, 0.04902926, 0.15630839, 0.16327935, 0.16374745, 0.69821732, 0.57546242),
"lower" = c( 0.16763051, 0.19599485, 0.20916489, 0.19420653, -0.07511518, -0.12563425, 0.57892260, 0.51191176),
"upper" = c(0.3493751, 0.8108237, 0.4013561, 0.8069241, 0.5649281, 0.5162440, 3.3158842, 2.7676830),
"p.value" = c(NA, NA, NA, NA, 0.1336343, 0.2329791, 0.1748165, 0.2662254)
)
test <- as.data.table(e.ateRR)
test <- test[order(factor(test$type, levels = unique(test$type)),test$time)]
test$estimator <- as.character(test$estimator)
expect_equal(ignore_attr=TRUE,test, GS, tol = 1e-4)
e.ateRR <- ate(event = CSC(Hist(time,event) ~ X1 + X2 + X3, data = dtS),
treatment = "X1",
data = dtS, times = 3:4, verbose = FALSE, cause = 1
)
})
## ** Tied events
n <- 500
set.seed(10)
dt <- sampleData(n, outcome="survival")
dt$time <- round(dt$time,1)+0.1
dt$X1 <- factor(rbinom(n, prob = c(0.4) , size = 1), labels = paste0("T",0:1))
test_that("ate in presence of ties",{
## Brice: Tuesday, Sept 1, 2020 11:10
m.event <- coxph(Surv(time,event)~ X1,data=dt,x=TRUE,y=TRUE)
ateRobust <- ate(event = m.event,
treatment = "X1",
data = dt, times = 1,
cause = 1, product.limit = FALSE,
verbose = FALSE)
iPred <- predictCox(m.event, newdata = unique(dt[,.SD,.SDcols = "X1"]),
times = 1, se = TRUE, type = "survival", keep.newdata = TRUE)
iDT <- as.data.table(iPred)
setkeyv(iDT, "X1")
expect_equal(ignore_attr=TRUE,ateRobust$meanRisk$estimate, 1-iDT$survival, tol = 1e-7)
expect_equal(ignore_attr=TRUE,ateRobust$meanRisk$se, iDT$survival.se, tol = 1e-7)
})
dt <- data.table("time" = c( 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.002, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.004, 0.005, 0.005, 0.005, 0.005, 0.005, 0.006, 0.006, 0.006, 0.006, 0.007, 0.007, 0.007, 0.007, 0.007, 0.007, 0.007, 0.007, 0.008, 0.008, 0.009, 0.009, 0.009, 0.010, 0.010, 0.010, 0.011, 0.011, 0.011, 0.012, 0.013, 0.014, 0.014, 0.015, 0.015, 0.016, 0.016, 0.016, 0.016, 0.016, 0.016, 0.016, 0.016, 0.017, 0.017, 0.017, 0.018, 0.018, 0.019, 0.019, 0.020, 0.021, 0.024, 0.024, 0.025, 0.025, 0.025, 0.025, 0.027, 0.027, 0.027, 0.029, 0.031, 0.032, 0.032, 0.033, 0.034, 0.034, 0.038, 0.040, 0.041, 0.042, 0.043, 0.043, 0.043, 0.048, 0.048, 0.049, 0.049, 0.049, 0.050, 0.052, 0.052, 0.053, 0.054, 0.056, 0.057, 0.059, 0.059, 0.060, 0.060, 0.061, 0.065, 0.066, 0.067, 0.067, 0.069, 0.069, 0.069, 0.070, 0.070, 0.071, 0.073, 0.074, 0.075, 0.076, 0.077, 0.080, 0.083, 0.090, 0.090, 0.095, 0.096, 0.101, 0.102, 0.105, 0.107, 0.109, 0.113, 0.114, 0.115, 0.117, 0.130, 0.131, 0.132, 0.135, 0.137, 0.139, 0.140, 0.144, 0.145, 0.148, 0.151, 0.152, 0.153, 0.155, 0.157, 0.159, 0.160, 0.160, 0.161, 0.163, 0.164, 0.167, 0.170, 0.179, 0.184, 0.186, 0.190, 0.199, 0.205, 0.209, 0.211, 0.213, 0.216, 0.219, 0.221, 0.222, 0.233, 0.238, 0.246, 0.251, 0.260, 0.262, 0.263, 0.273, 0.290, 0.296, 0.304, 0.310, 0.325, 0.333, 0.337, 0.371, 0.394, 0.422, 0.436, 0.444, 0.455, 0.467, 0.471, 0.486, 0.487, 0.518, 0.542, 0.555, 0.569, 0.590, 0.601, 0.602, 0.623, 0.635, 0.645, 0.649, 0.667, 0.672, 0.674, 0.712, 0.716, 0.731, 0.735, 0.745, 0.761, 0.782, 0.793, 0.800, 0.830, 0.857, 0.862, 0.887, 0.902, 0.964, 1.025, 1.110, 1.114, 1.169, 1.170, 1.179, 1.182, 1.189, 1.262, 1.274, 1.291, 1.297, 1.395, 1.462, 1.471, 1.516, 1.541, 1.547, 1.553, 1.621, 1.634, 1.667, 1.677, 1.681, 1.838, 1.872, 1.920, 2.022, 2.023, 2.035, 2.080, 2.101, 2.112, 2.313, 2.321, 2.343, 2.497, 2.507, 2.522, 2.572, 2.574, 2.591, 2.598, 2.668, 2.753, 2.908, 2.969, 2.971, 2.987, 3.006, 3.212, 3.243, 3.338, 3.586, 3.649, 3.750, 3.773, 3.977, 4.124, 4.173, 4.294, 4.298, 4.341, 4.645, 4.654, 4.722, 4.738, 4.906, 4.946, 5.109, 5.129, 5.294, 5.407, 5.601, 5.758, 5.807, 6.057, 6.068, 6.081, 6.200, 6.321, 6.411, 6.458, 6.494, 6.793, 6.882, 6.898, 6.977, 7.315, 7.426, 7.515, 7.643, 7.717, 8.024, 8.095, 8.203, 8.529, 8.591, 8.778, 9.919, 10.349, 11.364, 11.389, 11.471, 11.656, 12.082, 12.569, 12.902, 13.713, 14.194, 14.529, 14.596, 14.781, 14.881, 15.141, 15.342, 15.527, 15.854, 16.515, 16.999, 17.234, 17.287, 17.408, 18.575, 18.649, 19.089, 19.111, 19.265, 21.040, 21.416, 22.516, 22.571, 22.644, 22.913, 23.489, 24.640, 25.217, 25.250, 25.369, 25.524, 26.290, 27.315, 27.369, 28.162, 28.403, 28.548, 28.922, 28.954, 29.034, 29.634, 32.449, 32.667, 32.910, 33.566, 33.991, 34.409, 35.091, 35.177, 35.548, 37.138, 37.881, 40.640, 41.107, 42.194, 44.361, 48.559, 48.631, 49.985, 51.595, 52.309, 53.152, 54.773, 55.377, 56.479, 56.857, 59.070, 60.178, 60.677, 62.594, 64.336, 65.486, 65.750, 67.909, 69.234, 69.538, 69.764),
"cause" = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
"x1" = c(1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0),
"x2" = c(0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1),
"x3" = c(0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0),
"x4" = c( 1.34, 0.70, 3.06, 1.75, 2.65, -1.01, 1.84, 1.79, 1.20, 3.44, 0.03, 1.75, 1.73, 1.06, 1.52, 0.61, -0.64, -0.08, -0.14, 1.15, 0.33, 0.87, 2.07, 2.19, 0.28, 0.88, 0.84, 2.13, 1.84, 1.43, 0.91, 2.16, 0.47, 0.70, 2.06, 1.53, -0.75, 0.40, 1.62, 0.78, 2.34, 0.94, 0.66, 1.72, 0.50, 0.62, -0.55, 1.17, 0.56, 0.87, 1.77, 0.27, 1.23, -0.42, 2.46, 0.27, -0.29, 2.22, -0.24, 2.13, -0.88, 0.31, 0.23, 0.87, 0.70, 1.20, 2.27, 0.70, 0.53, 1.75, 1.94, 0.69, 0.09, 0.70, -0.10, 0.73, 0.31, 1.94, -0.50, 0.94, -0.21, 1.41, 1.19, -0.26, 0.35, 1.23, 0.76, 0.91, -0.07, 1.65, 0.02, -0.09, 0.67, 0.72, -0.23, 0.95, 0.81, 0.53, 1.77, 0.67, -0.26, 0.09, -0.56, 1.35, -0.12, 1.18, -0.55, 1.12, 0.03, 1.10, 0.87, 0.97, 1.16, -1.10, 0.42, 1.30, -0.42, 1.16, 1.96, 1.07, 0.39, 0.96, 0.93, 0.64, -0.80, 1.02, 0.63, 1.35, -0.84, 0.88, 0.57, 0.05, 0.14, 1.41, -1.71, 0.56, 0.87, 0.77, 0.03, 1.75, -0.54, 0.24, 0.32, 0.26, -0.39, 0.74, 0.19, 0.02, -0.31, 0.45, 0.35, 2.49, -0.73, 0.30, -0.49, -0.10, 1.24, 0.08, 0.09, -0.41, -0.02, 0.13, 1.13, 1.47, 1.15, 0.70, 0.60, -0.44, 0.98, -0.94, 0.07, 1.29, -0.71, 0.34, 0.10, 1.32, -0.10, -0.08, -0.74, 0.77, -0.09, -0.49, 1.09, 0.23, -0.69, 1.09, 0.44, 0.33, -0.60, 0.93, -0.28, -1.01, 0.34, 1.62, 0.57, 1.20, 0.65, -0.38, 0.36, 0.66, 0.73, -0.04, 0.72, -0.26, -0.26, 0.63, -0.02, 0.51, 0.37, 0.71, -0.01, 0.06, -0.40, 0.44, 1.08, -0.22, 0.40, 0.47, 0.66, -0.93, 0.16, 0.89, 1.40, -0.18, -0.05, 0.16, -0.95, -1.49, 1.12, 0.87, -0.56, 1.00, -0.51, 0.78, 0.17, 0.46, 1.37, -0.15, -0.82, 0.79, -0.35, -0.67, -0.48, -0.22, -0.50, 1.67, -0.83, -0.97, 1.27, -0.86, 0.60, 0.30, 0.46, 0.23, -0.41, 1.36, 0.53, -1.60, 0.77, 0.32, -0.88, 0.65, 0.07, 0.51, 1.42, -0.39, 0.15, 0.34, -0.09, 0.31, 0.67, -1.20, 1.48, -1.30, 0.92, 1.34, -0.96, 0.45, 0.32, -1.44, 0.68, 0.35, -0.46, -0.16, 0.04, 0.23, 0.23, 0.48, -0.20, 0.05, -0.27, -0.50, -0.02, -0.27, 0.23, 0.10, 0.70, 0.06, -0.66, 0.39, 0.19, 0.83, -1.04, 0.21, -0.53, -0.55, -1.88, 0.66, -1.01, -1.02, -1.46, -1.41, -0.84, -0.66, -0.13, -1.11, -0.03, -0.01, 0.48, -0.83, 0.00, 1.10, 0.12, -0.60, -0.64, -0.65, -1.11, -0.20, -0.73, -0.11, 0.74, -1.80, -0.34, -0.01, -0.20, -0.76, -1.63, 0.17, -0.87, 0.89, -0.71, -0.67, 0.06, -0.30, 0.50, 0.30, -1.29, -1.05, -0.12, -0.82, -0.79, -0.15, 0.74, -0.64, -0.76, 0.21, 0.65, 0.39, -0.49, 0.40, -0.58, -0.66, 0.29, 0.87, 0.19, -0.55, -1.08, 0.09, -0.74, -2.36, 0.14, 1.27, -0.39, 0.10, -0.91, -1.15, -1.15, -0.74, -0.64, -1.29, 0.07, -0.77, -0.82, -0.10, -0.47, -0.29, -1.55, -0.25, -0.42, -0.36, -1.00, -1.36, -1.78, 0.50, -0.91, -0.28, 0.35, -0.84, -1.85, 0.65, -0.90, 0.61, 1.01, -0.90, 0.11, 0.20, 0.50, -0.87, -1.91, -1.95, -0.43, 0.04, -0.98, -1.68, -1.29, -1.77, -0.17, -1.38, -0.64, -1.26, -0.96, -0.11, -0.59, -1.31, -0.12, -1.11, 0.11, -1.53, -1.98, 0.27, -0.17, -0.51, -2.15, 0.65, 0.13, -0.50, -0.47, -2.45, -0.77, -1.51, -0.02, -0.86, -1.75, -1.49, -0.16, -0.47, -1.78, -0.77, -0.03, -0.70, -0.92, -0.10, -0.37, -0.38, -1.56, 0.40, -0.19, -2.58, 0.66, -1.72, -1.17, -0.55, -1.25, -0.69, -1.90, -0.76, -1.44, -2.54, -1.60, -2.17, -1.52, -0.27, -2.74, -1.45, -0.41, -1.17, -1.80, 1.32, -0.41, -1.30, 0.27, -0.03, -1.87, -0.60, -0.85, -1.16, -1.12, -1.18, -1.61, -1.43, -0.74, -2.19, -0.77, -1.69, -1.53, -1.12, -1.36, -0.05, -1.31, -1.86),
"x5" = c( 1.90, -0.33, 0.64, 0.62, 0.56, 1.35, 1.21, -0.58, -0.54, 0.74, 1.22, 2.29, -0.39, 1.53, 1.33, 2.36, 1.50, 0.44, 2.17, 1.40, 0.15, 1.19, 1.83, 0.35, 0.78, 1.45, 0.72, 2.33, 1.05, 0.87, -0.01, 0.40, 0.69, 1.13, 0.38, -0.31, 2.88, 1.20, 0.99, 0.48, 0.49, 0.79, 0.45, 0.10, 0.75, 0.12, 1.54, 0.98, -0.36, 0.81, 0.00, 0.14, 0.30, 1.40, -0.29, 0.19, 0.83, 0.63, 0.64, 0.16, 1.12, 1.34, 1.77, 1.32, -0.49, 1.53, 0.00, 1.66, 0.88, 0.23, -0.22, -1.02, 1.29, 0.39, -0.17, -0.59, 0.44, 0.52, 0.66, 2.13, 0.97, 1.50, -0.67, 0.53, 0.88, -0.88, -0.60, 0.71, 1.24, 0.34, 1.43, 0.25, 1.14, 0.19, 0.89, 0.87, 0.15, -0.41, -0.70, 0.85, -0.03, -1.06, 0.35, -0.62, 0.82, 1.23, -0.30, 1.44, 0.41, -0.57, -1.61, 0.17, -0.91, 1.03, 1.21, 0.36, 0.53, -0.99, -1.03, 0.66, 1.41, -0.77, 0.74, -0.39, 1.63, -1.51, 0.60, -0.02, 0.06, 0.54, 1.23, -0.19, 0.21, 0.11, 0.13, -0.08, 0.74, 0.11, 0.15, 0.49, 0.74, 0.37, 0.28, 0.24, -0.22, -0.73, -0.41, 0.60, 0.24, 0.49, 0.64, -0.66, 0.38, 0.80, 1.00, 1.25, 0.05, 1.38, 0.98, 0.83, 0.76, -0.68, -0.64, 0.11, -1.05, 0.14, -0.37, 0.50, -0.17, 1.11, -1.83, 0.53, 0.95, 0.57, 0.37, 0.80, -0.39, 0.30, 0.90, 0.43, 0.47, 0.81, 0.73, 0.07, 1.59, 0.26, 0.58, -1.46, 1.26, 0.41, 0.28, 1.83, -0.58, -0.26, -0.87, -1.10, -1.19, 0.53, 0.18, 0.54, 0.68, 0.37, -1.66, 0.35, 1.25, -0.27, -0.57, -0.32, 0.90, 0.35, -0.33, 0.16, -0.22, -0.09, -1.29, -0.27, -1.28, -0.93, -1.18, -0.66, 0.10, 0.07, -1.39, -0.37, 0.89, 0.02, 0.68, 0.22, -0.07, -1.07, -0.90, 0.31, -0.40, -0.49, 0.23, -0.16, 0.33, -0.10, -0.64, 0.33, 0.70, 0.66, 0.18, 0.28, -0.96, 0.25, -0.07, 0.94, -1.18, -0.16, -0.33, 1.21, -0.91, 0.41, -1.05, 0.38, -1.00, 0.41, -0.80, -0.64, 1.45, 0.22, 0.76, 0.12, -0.08, -0.31, -0.11, -1.09, 0.53, 0.28, -1.05, 0.72, -0.68, 0.33, -0.62, -2.08, -0.51, -0.41, 0.48, 1.17, -0.48, -0.90, 0.16, 1.47, 0.87, -1.06, -1.17, -2.10, 0.77, -0.93, 0.09, 0.18, -0.30, -0.97, -0.02, -1.00, -0.69, -1.24, -0.57, -0.99, 0.59, 0.44, 1.04, 0.69, -0.69, -0.26, 0.91, -1.30, -0.27, 0.85, 1.59, 1.62, 0.75, 0.61, 0.32, 0.37, -0.57, -0.22, -1.98, 0.51, 0.74, -0.01, -1.84, -0.13, -0.40, -0.79, 0.14, 0.09, 0.23, -1.15, -1.39, 1.03, 1.70, 0.01, -1.05, -0.09, 1.61, -1.91, 0.13, -0.81, -1.01, 1.14, -1.07, -0.22, -0.27, -1.61, -0.75, -0.96, 0.54, 0.98, -0.86, 0.58, -0.34, -0.58, -0.26, -1.61, -0.38, -0.19, 0.33, 0.25, -0.15, -0.22, -0.04, -1.49, 0.30, -0.29, 0.03, -1.06, -0.11, 0.84, -1.18, -0.66, 0.46, 0.20, -0.56, 1.07, 0.52, -1.04, 0.31, -0.47, -1.17, -0.96, -0.94, -0.84, -1.18, 0.35, -0.99, 0.42, 0.07, 0.16, 0.78, -0.94, -1.57, 0.49, -1.41, -1.65, -0.86, -0.87, 2.31, -0.53, -0.50, -0.68, -1.33, -0.04, -0.03, -0.15, -2.59, -1.10, 1.44, 0.89, -0.75, -0.86, 0.16, 0.76, -1.10, 0.96, -1.74, 1.01, -0.77, 0.51, -0.18, 0.62, -1.75, -0.31, -1.92, -1.35, -1.17, 0.39, 0.26, -1.34, -1.79, -0.99, 0.27, -2.55, -0.02, -1.14, -0.17, -0.38, -0.78, -0.18, -0.34, 0.04, -0.80, -0.64, -1.64, -0.69, -0.17, 0.50, 0.03, 1.10, 0.64, -1.55, -0.68, -1.09, -0.50, -1.49, -1.55, 1.12, -2.14, -1.92, -0.46, 0.38, -0.63, 0.92, -0.79, 1.39, -0.65, -0.85, -0.03, -1.70, -1.05, -1.42, 1.53, -1.14, -0.38, -1.92, -1.17, -2.71, -1.36, 0.16, -0.32, -0.94, 1.33, -0.20, -0.67, -0.73, -2.00, -2.05, -1.98, 0.18, -1.57, -0.53, -1.47, 0.72, 0.04, -0.71, -1.16, -1.56, -1.94, -0.32),
"group" = c("1", "0", "0", "1", "0", "1", "1", "0", "1", "1", "1", "1", "1", "0", "1", "0", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "1", "1", "1", "0", "1", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1", "1", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "0", "1", "0", "1", "0", "1", "0", "1", "0", "0", "1", "0", "1", "1", "1", "0", "1", "1", "0", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "0", "1", "1", "1", "1", "0", "0", "1", "1", "0", "1", "0", "0", "1", "1", "0", "0", "1", "0", "0", "0", "1", "1", "1", "0", "1", "1", "0", "1", "0", "1", "0", "1", "0", "0", "0", "1", "0", "1", "1", "1", "1", "1", "0", "1", "0", "1", "1", "0", "1", "0", "1", "1", "0", "0", "0", "0", "1", "1", "0", "0", "0", "0", "1", "0", "0", "0", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1", "0", "0", "0", "1", "0", "0", "1", "1", "0", "0", "1", "0", "1", "0", "0", "1", "1", "1", "1", "1", "0", "0", "1", "0", "0", "0", "0", "1", "0", "0", "1", "1", "1", "0", "1", "0", "0", "1", "0", "1", "0", "1", "1", "0", "0", "1", "1", "0", "1", "1", "1", "0", "0", "1", "1", "0", "0", "0", "1", "1", "0", "0", "1", "0", "1", "0", "1", "1", "0", "1", "1", "0", "0", "0", "1", "0", "0", "0", "1", "0", "1", "0", "0", "0", "0", "0", "0", "0", "1", "1", "0", "0", "1", "1", "0", "1", "1", "1", "0", "1", "1", "0", "1", "0", "1", "1", "1", "1", "0", "0", "1", "1", "1", "1", "0", "1", "1", "0", "0", "1", "0", "1", "0", "0", "1", "0", "0", "0", "1", "1", "1", "0", "1", "0", "1", "1", "0", "0", "0", "0", "0", "0", "1", "1", "1", "1", "0", "0", "0", "1", "0", "0", "0", "1", "1", "0", "1", "1", "1", "0", "1", "1", "0", "0", "0", "1", "1", "1", "0", "1", "0", "0", "0", "1", "0", "1", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "1", "1", "1", "1", "0", "0", "1", "1", "1", "0", "1", "0", "1", "1", "0", "1", "1", "1", "0", "0", "1", "1", "0", "0", "0", "0", "1", "0", "1", "1", "1", "1", "1", "0", "1", "0", "0", "0", "1", "1", "1", "1", "0", "1", "0", "0", "0", "1", "0", "1", "1", "1", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0", "1", "0", "1", "1", "1", "0", "0", "1", "1", "1", "1", "1", "0", "1", "1", "0", "0", "0", "0", "1", "0", "0", "1", "0", "0", "0", "1", "1", "0", "0", "0", "1", "0", "1", "0", "0", "1", "0", "1", "0", "0", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "1", "0", "1", "0", "0", "1", "0", "0"))
dt$group <- as.factor(dt$group)
test_that("no error", { ## firs censoring happens at 1 which used to cause problem when evaluating at Gc at t-
## ** Between first and second censoring time
## ISSUE 18 (Github) ate function produces errors and NaN values when using method = "AIPTW"
## RobinDenz1
e.outcome <- coxph(Surv(time, cause) ~ x1 + x2 + x4 + x5 + group, data=dt, x=TRUE)
e.censoring <- coxph(Surv(time, cause==0) ~ group, data=dt, x=TRUE)
e.treatment <- glm(group ~ 1, data=dt, family="binomial")
predS <- predictCox(e.outcome, newdata = dt, time = 0.15)$survival
## range(predS)
testBefore1 <- ate(event = e.outcome,
treatment = e.treatment,
censor = e.censoring,
data = dt,
times=0.9,
se=TRUE, verbose = FALSE,
estimator="AIPTW")
expect_equal(ignore_attr=TRUE,as.data.table(testBefore1)$estimate, c(0.5701832, 0.6732129, 0.1030297, 1.1806959), tol = 1e-6)
expect_equal(ignore_attr=TRUE,as.data.table(testBefore1)$se, c(0.02590195, 0.02512034, 0.02773938, 0.05370323), tol = 1e-6)
## for times > 1 used to return an error
testAfter1 <- ate(event = e.outcome,
treatment = e.treatment,
censor = e.censoring,
data = dt,
times=1.5,
se=TRUE, verbose = FALSE,
estimator="AIPTW")
## for times > 1 it returns NaN only
expect_equal(ignore_attr=TRUE,as.data.table(testAfter1)$estimate, c(0.60287336, 0.69921301, 0.09633966, 1.15980082), tol = 1e-6)
expect_equal(ignore_attr=TRUE,as.data.table(testAfter1)$se, c(0.02559758, 0.02458960, 0.02724511, 0.04936567), tol = 1e-6)
})
# }}}
######################################################################
### test-ate.R ends here
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