tests/testthat/test-cmest.R
In LindaValeri/CMAverse: Causal Mediation Analysis
context("cmest estimates causal effects correctly")
test_that("cmest works correctly for binary Y and continuous M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 100
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rnorm(n, 1 + 2*A + 1.5*C1 + 0.8*C2, 1)
py <- expit(-1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
data <- data.frame(A, M, Y, C1, C2)
# results of cmest
res_bincont <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("linear"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
# reference results
yreg <- glm(Y ~ A*M + C1 + C2, family = binomial, data = data)
mreg <- lm(M ~ A + C1 + C2, data = data)
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
s <- sigma(mreg)
cde_bincont <- exp((theta1+theta3*m)*(a-astar))
pnde_bincont <- exp((theta1+theta3*(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2*s^2))*(a-astar)+0.5*theta3^2*s^2*(a-astar)^2)
tnde_bincont <- exp((theta1+theta3*(beta0+beta1*a+beta2*meanc1+beta3*meanc2+theta2*s^2))*(a-astar)+0.5*theta3^2*s^2*(a-astar)^2)
pnie_bincont <- exp((theta2*beta1+theta3*beta1*astar)*(a-astar))
tnie_bincont <- exp((theta2*beta1+theta3*beta1*a)*(a-astar))
te_bincont <- pnde_bincont*tnie_bincont
cde_err_bincont <- (exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
exp(theta2*m-(theta2+theta3*astar)*(beta0+beta1*astar+beta2*meanc1+beta3*meanc2)-0.5*(theta2+theta3*astar)^2*s^2)
intref_err_bincont <- pnde_bincont - 1 - cde_err_bincont
intmed_err_bincont <- tnie_bincont * pnde_bincont - pnde_bincont - pnie_bincont + 1
pnie_err_bincont <- pnie_bincont - 1
total_err_bincont <- te_bincont - 1
cde_err_prop_bincont <- cde_err_bincont/total_err_bincont
intmed_err_prop_bincont <- intmed_err_bincont/total_err_bincont
intref_err_prop_bincont <- intref_err_bincont/total_err_bincont
pnie_err_prop_bincont <- pnie_err_bincont/total_err_bincont
pm_bincont <- (pnie_err_bincont+intmed_err_bincont)/total_err_bincont
int_bincont <- (intref_err_bincont+intmed_err_bincont)/total_err_bincont
pe_bincont <- (intref_err_bincont+intmed_err_bincont+pnie_err_bincont)/total_err_bincont
ref <- c(cde_bincont, pnde_bincont, tnde_bincont, pnie_bincont, tnie_bincont, te_bincont, cde_err_bincont,
intref_err_bincont, intmed_err_bincont, pnie_err_bincont, cde_err_prop_bincont,
intref_err_prop_bincont, intmed_err_prop_bincont, pnie_err_prop_bincont,
pm_bincont, int_bincont, pe_bincont)
# test
expect_equal(unname(res_bincont$effect.pe), ref)
})
test_that("cmest works correctly for continuous Y and continuous M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rnorm(n, 1 + 2*A + 1.5*C1 + 0.8*C2, 1)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
# results of cmest
res_contcont <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("linear"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
# reference results
yreg <- lm(Y ~ A*M + C1 + C2, data = data)
mreg <- lm(M ~ A + C1 + C2, data = data)
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_contcont <- (theta1+theta3*m)*(a-astar)
pnde_contcont <- (theta1+theta3*(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(a-astar)
tnde_contcont <- (theta1+theta3*(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(a-astar)
pnie_contcont <- (theta2*beta1+theta3*beta1*astar)*(a-astar)
tnie_contcont <- (theta2*beta1+theta3*beta1*a)*(a-astar)
te_contcont <- pnde_contcont+tnie_contcont
intref_contcont <- pnde_contcont-cde_contcont
intmed_contcont <- tnie_contcont-pnie_contcont
cde_prop_contcont <- cde_contcont/te_contcont
intref_prop_contcont <- intref_contcont/te_contcont
intmed_prop_contcont <- intmed_contcont/te_contcont
pnie_prop_contcont <- pnie_contcont/te_contcont
pm_contcont <- (pnie_contcont+intmed_contcont)/te_contcont
int_contcont <- (intref_contcont+intmed_contcont)/te_contcont
pe_contcont <- (intref_contcont+intmed_contcont+pnie_contcont)/te_contcont
ref <- c(cde_contcont, pnde_contcont, tnde_contcont, pnie_contcont, tnie_contcont, te_contcont,
intref_contcont, intmed_contcont, cde_prop_contcont, intref_prop_contcont, intmed_prop_contcont,
pnie_prop_contcont, pm_contcont, int_contcont, pe_contcont)
# test
expect_equal(unname(res_contcont$effect.pe), ref)
})
test_that("cmest works correctly for continuous Y and binary M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
yreg <- lm(Y ~ A*M + C1 + C2, data = data)
mreg <- glm(M ~ A + C1 + C2, family = binomial, data = data)
# results of cmest
res_contbin_rb_param_delta <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
res_contbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
# mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
# mreg = list("multinomial"), yreg = "linear",
# astar = 0, a = 1, mval = list(1),
# estimation = "imputation", inference = "bootstrap")
res_contbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# reference results
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_contbin <- (theta1+theta3*m)*(a-astar)
pnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2)
tnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)
pnie_contbin <- (theta2+theta3*astar)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
tnie_contbin <- (theta2+theta3*a)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
te_contbin <- pnde_contbin+tnie_contbin
intref_contbin <- pnde_contbin-cde_contbin
intmed_contbin <- tnie_contbin-pnie_contbin
cde_prop_contbin <- cde_contbin/te_contbin
intref_prop_contbin <- intref_contbin/te_contbin
intmed_prop_contbin <- intmed_contbin/te_contbin
pnie_prop_contbin <- pnie_contbin/te_contbin
pm_contbin<- (pnie_contbin+intmed_contbin)/te_contbin
int_contbin <- (intref_contbin+intmed_contbin)/te_contbin
pe_contbin <- (intref_contbin+intmed_contbin+pnie_contbin)/te_contbin
ref <- c(cde_contbin, pnde_contbin, tnde_contbin, pnie_contbin, tnie_contbin, te_contbin,
intref_contbin, intmed_contbin, cde_prop_contbin, intref_prop_contbin, intmed_prop_contbin,
pnie_prop_contbin, pm_contbin, int_contbin, pe_contbin)
# test
expect_equal(unname(res_contbin_rb_param_delta$effect.pe), ref)
expect_equal(unname(res_contbin_rb_param_bootstrap$effect.pe), ref)
expect_equal(unname(res_contbin_rb_impu_bootstrap$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_wb$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_msm$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_ne$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_iorw$effect.pe), ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(unname(res_contbin_gformula$effect.pe), ref, tolerance = 0.1)
})
test_that("cmest works correctly for multiple imputations", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
yreg <- lm(Y ~ A*M + C1 + C2, data = data)
mreg <- glm(M ~ A + C1 + C2, family = binomial, data = data)
# results of cmest
res_contbin_rb_param_delta <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta", multimp = TRUE)
res_contbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap", multimp = TRUE)
res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
# reference results
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_contbin <- (theta1+theta3*m)*(a-astar)
pnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2)
tnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)
pnie_contbin <- (theta2+theta3*astar)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
tnie_contbin <- (theta2+theta3*a)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
te_contbin <- pnde_contbin+tnie_contbin
intref_contbin <- pnde_contbin-cde_contbin
intmed_contbin <- tnie_contbin-pnie_contbin
cde_prop_contbin <- cde_contbin/te_contbin
intref_prop_contbin <- intref_contbin/te_contbin
intmed_prop_contbin <- intmed_contbin/te_contbin
pnie_prop_contbin <- pnie_contbin/te_contbin
pm_contbin<- (pnie_contbin+intmed_contbin)/te_contbin
int_contbin <- (intref_contbin+intmed_contbin)/te_contbin
pe_contbin <- (intref_contbin+intmed_contbin+pnie_contbin)/te_contbin
ref <- c(cde_contbin, pnde_contbin, tnde_contbin, pnie_contbin, tnie_contbin, te_contbin,
intref_contbin, intmed_contbin, cde_prop_contbin, intref_prop_contbin, intmed_prop_contbin,
pnie_prop_contbin, pm_contbin, int_contbin, pe_contbin)
# test
expect_equal(unname(res_contbin_rb_param_delta$effect.pe), ref)
expect_equal(unname(res_contbin_rb_param_bootstrap$effect.pe), ref)
expect_equal(unname(res_contbin_rb_impu_bootstrap$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_wb$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_msm$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_ne$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_iorw$effect.pe), ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(unname(res_contbin_gformula$effect.pe), ref, tolerance = 0.1)
})
test_that("cmest works correctly for bca", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
# results of cmest
res_contbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
# test
ref <- summary(res_contbin_rb_param_bootstrap)$summarydf$Estimate
expect_equal(summary(res_contbin_rb_impu_bootstrap)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_wb)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_msm)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_ne)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_iorw)$summarydf$Estimate, ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(summary(res_contbin_gformula)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(class(print(res_contbin_rb_param_bootstrap)), "list")
expect_equal(class(print(res_contbin_rb_impu_bootstrap)), "list")
expect_equal(class(print(res_contbin_wb)), "list")
expect_equal(class(print(res_contbin_msm)), "list")
expect_equal(class(print(res_contbin_ne)), "list")
expect_equal(class(print(res_contbin_iorw)), "list")
expect_equal(class(print(res_contbin_gformula)), "list")
expect_equal(class(print(summary(res_contbin_rb_impu_bootstrap))), "list")
expect_equal(class(print(summary(res_contbin_wb))), "list")
expect_equal(class(print(summary(res_contbin_msm))), "list")
expect_equal(class(print(summary(res_contbin_ne))), "list")
expect_equal(class(print(summary(res_contbin_iorw))), "list")
expect_equal(class(print(summary(res_contbin_gformula))), "list")
})
test_that("cmest works correctly for survival Y and count M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 - 0.3*C2)
A <- rbinom(n, 1, pa)
M <- rpois(n, exp(1 + 0.2*A + 0.2*C1 + 0.1*C2))
Y <- rexp(n,exp(-1 - 0.3*A - 0.1*M - 0.1*A*M + 0.3*C1 - 0.6*C2))
cen <- quantile(Y, probs = 0.01)
delta <- as.numeric(Y > cen)
data <- data.frame(A, M, Y, C1, C2, delta)
ereg <- glm(A ~ C1 + C2, family = binomial(), data = data)
yreg <- survival::survreg(survival::Surv(Y, delta) ~ A*M + C1 + C2, data = data, dist = "weibull")
mreg <- glm(M ~ A + C1 + C2, family = poisson(), data = data)
# results of cmest
# res_survcount_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
# exposure = "A", mediator = "M", basec = c("C1", "C2"),
# EMint = TRUE,
# mreg = list("linear"), yreg = "aft_exp",
# astar = 0, a = 1, mval = list(1),
# estimation = "paramfunc", inference = "delta")
res_survcount_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("poisson"), yreg = "aft_weibull",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survcount_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
ereg = "logistic", yreg = "aft_weibull",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_survcount_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
mreg = list("poisson"), yreg = "aft_weibull",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_survcount_rb$effect.pe)[c(6,2,5,15)],
unname(res_survcount_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survcount_rb$effect.pe),
unname(res_survcount_gformula$effect.pe), tolerance = 0.1)
# results of cmest
res_survcount_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("negbin"), yreg = "coxph",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survcount_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
ereg = "logistic", yreg = "coxph",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_survcount_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
mreg = list("negbin"), yreg = "coxph",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_survcount_rb$effect.pe)[c(6,2,5,15)],
unname(res_survcount_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survcount_rb$effect.pe),
unname(res_survcount_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for count Y and count M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 - 0.3*C2)
A <- rbinom(n, 1, pa)
M <- rpois(n, exp(1 + 0.2*A + 0.2*C1 + 0.1*C2))
Y <- rpois(n,exp(-1 - 0.3*A - 0.1*M - 0.1*A*M + 0.3*C1 - 0.6*C2))
data <- data.frame(A, M, Y, C1, C2)
ereg <- glm(A ~ C1 + C2, family = binomial(), data = data)
yreg <- glm(Y ~ A*M + C1 + C2, family = quasipoisson(), data = data)
mreg <- glm(M ~ A + C1 + C2, family = quasipoisson(), data = data)
# results of cmest
res_countcount_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("quasipoisson"), yreg = "quasipoisson",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_countcount_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "quasipoisson",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_countcount_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "quasipoisson",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_countcount_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("quasipoisson"), yreg = "quasipoisson",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_countcount_rb$effect.pe),
unname(res_countcount_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_countcount_rb$effect.pe)[c(6,2,5,15)],
unname(res_countcount_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_countcount_rb$effect.pe),
unname(res_countcount_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for survival Y and ordinal M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
proba1to2 <- expit(0.4 + 0.5*C1 - 0.3*C2)
proba2 <- expit(-0.8 + 0.5*C1 - 0.3*C2)
proba0 <- 1 - proba1to2
proba1 <- proba1to2 - proba2
A <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(proba0[x],
proba1[x],
proba2[x])))
A <- as.factor(A)
probm1to2 <- expit(0.5 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm2 <- expit(-1 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm0 <- 1 - probm1to2
probm1 <- probm1to2 - probm2
M <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(probm0[x],
probm1[x],
probm2[x])))
M <- as.factor(M)
Y <- rexp(n,exp(-1 - 0.3*(A == 1) - 0.1*(A == 2) - 0.1*(M == 1) - 0.3*(M == 2) -
0.1*(A == 1)*(M == 1) - 0.3*(A == 1)*(M == 2) -
0.2*(A == 2)*(M == 1) - 0.1*(A == 2)*(M == 2) +
0.3*C1 - 0.6*C2))
cen <- quantile(Y, probs = 0.01)
delta <- as.numeric(Y > cen)
data <- data.frame(A, M, Y, C1, C2, delta)
ereg <- MASS::polr(A ~ C1 + C2, data = data)
yreg <- survival::survreg(survival::Surv(Y, delta) ~ A*M + C1 + C2, data = data, dist = "exponential")
mreg <- MASS::polr(M ~ A + C1 + C2, data = data)
# results of cmest
res_survordinal_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("ordinal"), yreg = "aft_exp",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survordinal_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
ereg = "ordinal", yreg = "aft_exp",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_survordinal_msm <- cmest(data = data, model = "msm",
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "ordinal", yreg = "aft_exp", mreg = list("ordinal"),
wmnomreg = list("ordinal"), wmdenomreg = list("ordinal"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survordinal_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
mreg = list("ordinal"), yreg = "aft_exp",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_survordinal_rb$effect.pe)[c(6,2,5,15)],
unname(res_survordinal_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survordinal_rb$effect.pe),
unname(res_survordinal_msm$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survordinal_rb$effect.pe),
unname(res_survordinal_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for ordinal Y and ordinal M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
proba1to2 <- expit(0.4 + 0.5*C1 - 0.3*C2)
proba2 <- expit(-0.8 + 0.5*C1 - 0.3*C2)
proba0 <- 1 - proba1to2
proba1 <- proba1to2 - proba2
A <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(proba0[x],
proba1[x],
proba2[x])))
A <- as.factor(A)
probm1to2 <- expit(0.5 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm2 <- expit(-1 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm0 <- 1 - probm1to2
probm1 <- probm1to2 - probm2
M <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(probm0[x],
probm1[x],
probm2[x])))
M <- as.factor(M)
proby1to2 <- expit(1 - 0.3*(A == 1) - 0.1*(A == 2) - 0.1*(M == 1) - 0.3*(M == 2) -
0.1*(A == 1)*(M == 1) - 0.3*(A == 1)*(M == 2) -
0.2*(A == 2)*(M == 1) - 0.1*(A == 2)*(M == 2) +
0.3*C1 - 0.6*C2)
proby2 <- expit(-0.5 - 0.3*(A == 1) - 0.1*(A == 2) - 0.1*(M == 1) - 0.3*(M == 2) -
0.1*(A == 1)*(M == 1) - 0.3*(A == 1)*(M == 2) -
0.2*(A == 2)*(M == 1) - 0.1*(A == 2)*(M == 2) +
0.3*C1 - 0.6*C2)
proby0 <- 1 - proby1to2
proby1 <- proby1to2 - proby2
Y <- sapply(1:n, FUN = function(x) sample(c(0,1,2), size = 1, replace = TRUE,
prob=c(proby0[x],
proby1[x],
proby2[x])))
Y <- as.factor(Y)
data <- data.frame(A, M, Y, C1, C2)
ereg <- MASS::polr(A ~ C1 + C2, data = data)
yreg <- MASS::polr(Y ~ A*M + C1 + C2, data = data)
mreg <- MASS::polr(M ~ A + C1 + C2, data = data)
# results of cmest
res_ordinalordinal_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("ordinal"), yreg = "ordinal", yval = "1",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_ordinalordinal_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "ordinal", yreg = "ordinal", yval = "1",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_ordinalordinal_msm <- cmest(data = data, model = "msm",
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "ordinal", yreg = "ordinal", mreg = list("ordinal"),
wmnomreg = list("ordinal"), wmdenomreg = list("ordinal"),
astar = 0, a = 1, mval = list(1), yval = "1",
estimation = "imputation", inference = "bootstrap")
res_ordinalordinal_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("ordinal"), yreg = "ordinal",
astar = 0, a = 1, mval = list(1), yval = "1",
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_ordinalordinal_rb$effect.pe)[c(6,2,5)],
unname(res_ordinalordinal_iorw$effect.pe)[1:3], tolerance = 0.1)
expect_equal(unname(res_ordinalordinal_rb$effect.pe),
unname(res_ordinalordinal_msm$effect.pe), tolerance = 0.1)
expect_equal(unname(res_ordinalordinal_rb$effect.pe),
unname(res_ordinalordinal_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for continuous Y and gamma M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rgamma(n, shape = 2, scale = exp(-3 + 2*A + 1.5*C1 + 0.8*C2)/2)
Y <- rnorm(n, -3 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 0.5)
data <- data.frame(A, M, Y, C1, C2)
mreg <- glm(M ~ A + C1 + C2, data = data, family = Gamma("log"))
# results of cmest
res_gammalinear_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list(mreg), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_gammalinear_wb <- cmest(data = data, model = "wb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_gammalinear_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_gammalinear_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_gammalinear_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list(mreg), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_gammalinear_rb$effect.pe),
unname(res_gammalinear_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_gammalinear_rb$effect.pe)[c(6,2,5,15)],
unname(res_gammalinear_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_gammalinear_rb$effect.pe),
unname(res_gammalinear_ne$effect.pe), tolerance = 0.1)
expect_equal(unname(res_gammalinear_rb$effect.pe),
unname(res_gammalinear_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for multiple mediators", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M1 <- rbinom(n, 1, expit(1 + 2*A + 1.5*C1 + 0.8*C2))
M2 <- rpois(n, exp(1 + 0.2*A + 0.3*M1 + 0.2*C1 + 0.1*C2))
Y <- rnorm(n, -3 + 0.8*A - 1.8*M1 + 0.6*M2 + 0.5*A*M1 + 0.8*A*M2 + 0.3*C1 - 0.6*C2, 0.5)
data <- data.frame(A, M1, M2, Y, C1, C2)
# results of cmest
res_multipleM_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = c("M1", "M2"), basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("logistic", "poisson"), yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
res_multipleM_wb <- cmest(data = data, model = "wb", outcome = "Y",
exposure = "A", mediator = c("M1", "M2"), basec = c("C1", "C2"),
EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
res_multipleM_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_multipleM_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
res_multipleM_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic", "poisson"), yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_multipleM_rb$effect.pe),
unname(res_multipleM_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_multipleM_rb$effect.pe)[c(6,2,5,15)],
unname(res_multipleM_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_multipleM_rb$effect.pe),
unname(res_multipleM_ne$effect.pe), tolerance = 0.1)
expect_equal(unname(res_multipleM_rb$effect.pe),
unname(res_multipleM_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for binary Y and binary M with postc", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
L <- rnorm(n, mean = 0.5 - A + 0.2*C1 + 0.6*C2)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2 + L)
M <- rbinom(n, 1, pm)
py <- expit(-3 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2 + L)
Y <- rbinom(n, 1, py)
data <- data.frame(A, M, Y, C1, C2, L)
# results of cmest
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
res_binbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
expect_error(cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
res_binbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic", postcreg = list("linear"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(summary(res_binbin_gformula)$summarydf$Estimate,
summary(res_binbin_msm)$summarydf$Estimate, tolerance = 0.1)
expect_equal(class(print(res_binbin_msm)), "list")
expect_equal(class(print(res_binbin_gformula)), "list")
expect_equal(class(print(summary(res_binbin_msm))), "list")
expect_equal(class(print(summary(res_binbin_gformula))), "list")
res_binbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_binbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic", postcreg = list("linear"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
# test
expect_equal(summary(res_binbin_gformula)$summarydf$Estimate,
summary(res_binbin_msm)$summarydf$Estimate, tolerance = 0.1)
expect_equal(class(print(res_binbin_msm)), "list")
expect_equal(class(print(res_binbin_gformula)), "list")
expect_equal(class(print(summary(res_binbin_msm))), "list")
expect_equal(class(print(summary(res_binbin_gformula))), "list")
})
test_that("cmest works correctly for regressions with prior weights", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 1000000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M1 <- rbinom(n, 1, expit(0.5 + 0.2*A + 0.5*C1 - 0.8*C2))
sum(M1)
M2 <- rbinom(n, 1, expit(-1 + A + M1 - 0.5*C1 + 0.5*C2))
sum(M2)
py <- expit(-6 + 0.8*A - 1.2*M1 + 0.5*M2 + 0.3*A*M1 + 0.2*A*M2 + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
yprevalence <- sum(Y)/n
data <- data.frame(A, M1, M2, Y, C1, C2)
case_indice <- sample(which(data$Y == 1), 2000, replace = FALSE)
control_indice <- sample(which(data$Y == 0), 2000, replace = FALSE)
data <- data[c(case_indice, control_indice), ]
mreg1 <- glm(M1 ~ A + C1 + C2, data = data, family = binomial, weights = rep(2, 4000))
mreg2 <- glm(M2 ~ A + C1 + C2, data = data, family = binomial, weights = rep(2, 4000))
mreg1_msm <- glm(M1 ~ A, data = data, family = binomial, weights = rep(2, 4000))
mreg2_msm <- glm(M2 ~ A, data = data, family = binomial, weights = rep(2, 4000))
# yprevalence = yprevalence
# results of cmest
res_binbin_rb_impu_bootstrap <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
mreg = list(mreg1, mreg2), yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_wb <- cmest(data = data, model = "wb", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_iorw <- cmest(data = data, model = "iorw", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_msm <- cmest(data = data, model = "msm", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list(mreg1_msm, mreg2_msm),
wmnomreg = list("logistic", "logistic"), wmdenomreg = list("logistic", "logistic"),
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_ne <- cmest(data = data, model = "ne", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_gformula <- cmest(data = data, model = "gformula", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
mreg = list(mreg1, mreg2), yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe)[c(6,2,5,15)],
unname(res_binbin_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_ne$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_msm$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for binary Y and binary M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
py <- expit(-3 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
data <- data.frame(A, M, Y, C1, C2)
yreg <- glm(Y ~ A*M + C1 + C2, family = binomial(), data = data)
mreg <- glm(M ~ A + C1 + C2, family = binomial(), data = data)
# results of cmest
res_binbin_rb_param_delta <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "para", inference = "delt")
res_binbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
res_binbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "impu", inference = "boot")
res_binbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmdenomreg = list("logistic"), wmnomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# reference results
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_binbin <- exp((theta1+theta3*m)*(a-astar))
pnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))
pnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
te_binbin <- pnde_binbin*tnie_binbin
cde_err_binbin <- exp(theta2*m)*(exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))/
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2+theta3*astar))
intref_err_binbin <- pnde_binbin - 1 - cde_err_binbin
intmed_err_binbin <- tnie_binbin * pnde_binbin - pnde_binbin - pnie_binbin + 1
pnie_err_binbin <- pnie_binbin - 1
total_err_binbin <- te_binbin - 1
cde_err_prop_binbin <- cde_err_binbin/total_err_binbin
intmed_err_prop_binbin <- intmed_err_binbin/total_err_binbin
intref_err_prop_binbin <- intref_err_binbin/total_err_binbin
pnie_err_prop_binbin <- pnie_err_binbin/total_err_binbin
pm_binbin <- (pnie_err_binbin+intmed_err_binbin)/total_err_binbin
int_binbin <- (intref_err_binbin+intmed_err_binbin)/total_err_binbin
pe_binbin <- (intref_err_binbin+intmed_err_binbin+pnie_err_binbin)/total_err_binbin
ref <- c(cde_binbin, pnde_binbin, tnde_binbin, pnie_binbin, tnie_binbin, te_binbin, cde_err_binbin,
intref_err_binbin, intmed_err_binbin, pnie_err_binbin, cde_err_prop_binbin,
intref_err_prop_binbin, intmed_err_prop_binbin, pnie_err_prop_binbin,
pm_binbin, int_binbin, pe_binbin)
# test
expect_equal(summary(res_binbin_rb_param_delta)$summarydf$Estimate, ref)
expect_equal(class(ggcmest(res_binbin_rb_param_delta)), c("gg", "ggplot"))
expect_equal(summary(res_binbin_rb_param_bootstrap)$summarydf$Estimate, ref)
expect_equal(summary(res_binbin_rb_impu_bootstrap)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_wb)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_msm)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_ne)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_iorw)$summarydf$Estimate, ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(summary(res_binbin_gformula)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(class(print(res_binbin_rb_param_delta)), "list")
expect_equal(class(print(res_binbin_rb_param_bootstrap)), "list")
expect_equal(class(print(res_binbin_rb_impu_bootstrap)), "list")
expect_equal(class(print(res_binbin_wb)), "list")
expect_equal(class(print(res_binbin_msm)), "list")
expect_equal(class(print(res_binbin_ne)), "list")
expect_equal(class(print(res_binbin_iorw)), "list")
expect_equal(class(print(res_binbin_gformula)), "list")
expect_equal(class(print(summary(res_binbin_rb_param_delta))), "list")
expect_equal(class(print(summary(res_binbin_rb_param_bootstrap))), "list")
expect_equal(class(print(summary(res_binbin_rb_impu_bootstrap))), "list")
expect_equal(class(print(summary(res_binbin_wb))), "list")
expect_equal(class(print(summary(res_binbin_msm))), "list")
expect_equal(class(print(summary(res_binbin_ne))), "list")
expect_equal(class(print(summary(res_binbin_iorw))), "list")
expect_equal(class(print(summary(res_binbin_gformula))), "list")
})
test_that("cmest works correctly for binary Y and binary M in a case control study", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rbinom(n, 1, expit(1 + 2*A + 1.5*C1 + 0.8*C2))
py <- expit(-5 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
yprevalence <- sum(Y)/n
data <- data.frame(A, M, Y, C1, C2)
case_indice <- sample(which(data$Y == 1), 5000, replace = FALSE)
control_indice <- sample(which(data$Y == 0), 5000, replace = FALSE)
data <- data[c(case_indice, control_indice), ]
# yrare = TRUE
# results of cmest
res_binbin_rb_param_delta <- cmest(data = data, model = "rb", casecontrol = TRUE, yrare = TRUE,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
res_binbin_rb_param_bootstrap <- cmest(data = data, model = "rb", casecontrol = TRUE, yrare = TRUE,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
# reference results
thetas <- unname(coef(res_binbin_rb_param_delta$reg.output$yreg))
betas <- unname(coef(res_binbin_rb_param_delta$reg.output$mreg[[1]]))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(data$C1)
meanc2 <- mean(data$C2)
cde_binbin <- exp((theta1+theta3*m)*(a-astar))
pnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))
pnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
te_binbin <- pnde_binbin*tnie_binbin
cde_err_binbin <- exp(theta2*m)*(exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))/
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2+theta3*astar))
intref_err_binbin <- pnde_binbin - 1 - cde_err_binbin
intmed_err_binbin <- tnie_binbin * pnde_binbin - pnde_binbin - pnie_binbin + 1
pnie_err_binbin <- pnie_binbin - 1
total_err_binbin <- te_binbin - 1
cde_err_prop_binbin <- cde_err_binbin/total_err_binbin
intmed_err_prop_binbin <- intmed_err_binbin/total_err_binbin
intref_err_prop_binbin <- intref_err_binbin/total_err_binbin
pnie_err_prop_binbin <- pnie_err_binbin/total_err_binbin
pm_binbin <- (pnie_err_binbin+intmed_err_binbin)/total_err_binbin
int_binbin <- (intref_err_binbin+intmed_err_binbin)/total_err_binbin
pe_binbin <- (intref_err_binbin+intmed_err_binbin+pnie_err_binbin)/total_err_binbin
ref <- c(cde_binbin, pnde_binbin, tnde_binbin, pnie_binbin, tnie_binbin, te_binbin, cde_err_binbin,
intref_err_binbin, intmed_err_binbin, pnie_err_binbin, cde_err_prop_binbin,
intref_err_prop_binbin, intmed_err_prop_binbin, pnie_err_prop_binbin,
pm_binbin, int_binbin, pe_binbin)
# test
expect_equal(unname(res_binbin_rb_param_delta$effect.pe), ref)
expect_equal(unname(res_binbin_rb_param_bootstrap$effect.pe), ref)
# yprevalence = yprevalence
# results of cmest
res_binbin_rb_param_delta <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "loglinear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
res_binbin_rb_param_bootstrap <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "loglinear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
res_binbin_rb_impu_bootstrap <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_wb <- cmest(data = data, model = "wb", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_iorw <- cmest(data = data, model = "iorw", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_msm <- cmest(data = data, model = "msm", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_ne <- cmest(data = data, model = "ne", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_gformula <- cmest(data = data, model = "gformula", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# reference results
thetas <- unname(coef(res_binbin_rb_param_delta$reg.output$yreg))
betas <- unname(coef(res_binbin_rb_param_delta$reg.output$mreg[[1]]))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(data$C1)
meanc2 <- mean(data$C2)
cde_binbin <- exp((theta1+theta3*m)*(a-astar))
pnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))
pnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
te_binbin <- pnde_binbin*tnie_binbin
cde_err_binbin <- exp(theta2*m)*(exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))/
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2+theta3*astar))
intref_err_binbin <- pnde_binbin - 1 - cde_err_binbin
intmed_err_binbin <- tnie_binbin * pnde_binbin - pnde_binbin - pnie_binbin + 1
pnie_err_binbin <- pnie_binbin - 1
total_err_binbin <- te_binbin - 1
cde_err_prop_binbin <- cde_err_binbin/total_err_binbin
intmed_err_prop_binbin <- intmed_err_binbin/total_err_binbin
intref_err_prop_binbin <- intref_err_binbin/total_err_binbin
pnie_err_prop_binbin <- pnie_err_binbin/total_err_binbin
pm_binbin <- (pnie_err_binbin+intmed_err_binbin)/total_err_binbin
int_binbin <- (intref_err_binbin+intmed_err_binbin)/total_err_binbin
pe_binbin <- (intref_err_binbin+intmed_err_binbin+pnie_err_binbin)/total_err_binbin
ref <- c(cde_binbin, pnde_binbin, tnde_binbin, pnie_binbin, tnie_binbin, te_binbin, cde_err_binbin,
intref_err_binbin, intmed_err_binbin, pnie_err_binbin, cde_err_prop_binbin,
intref_err_prop_binbin, intmed_err_prop_binbin, pnie_err_prop_binbin,
pm_binbin, int_binbin, pe_binbin)
# test
expect_equal(unname(res_binbin_rb_param_delta$effect.pe), ref)
#expect_equal(unname(res_binbin_rb_param_delta_multinom$effect.pe), ref, tolerance = 0.001)
expect_equal(class(print(res_binbin_rb_param_delta)), "list")
#expect_equal(class(print(res_binbin_rb_param_delta_multinom)), "list")
expect_equal(unname(res_binbin_rb_param_bootstrap$effect.pe), ref)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_wb$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_msm$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_ne$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_iorw$effect.pe), ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(unname(res_binbin_gformula$effect.pe), ref, tolerance = 0.1)
#expect_equal(class(print(summary(res_binbin_rb_param_delta_multinom))), "list")
})
LindaValeri/CMAverse documentation built on July 16, 2024, 11:58 p.m.
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context("cmest estimates causal effects correctly")
test_that("cmest works correctly for binary Y and continuous M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 100
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rnorm(n, 1 + 2*A + 1.5*C1 + 0.8*C2, 1)
py <- expit(-1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
data <- data.frame(A, M, Y, C1, C2)
# results of cmest
res_bincont <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("linear"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
# reference results
yreg <- glm(Y ~ A*M + C1 + C2, family = binomial, data = data)
mreg <- lm(M ~ A + C1 + C2, data = data)
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
s <- sigma(mreg)
cde_bincont <- exp((theta1+theta3*m)*(a-astar))
pnde_bincont <- exp((theta1+theta3*(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2*s^2))*(a-astar)+0.5*theta3^2*s^2*(a-astar)^2)
tnde_bincont <- exp((theta1+theta3*(beta0+beta1*a+beta2*meanc1+beta3*meanc2+theta2*s^2))*(a-astar)+0.5*theta3^2*s^2*(a-astar)^2)
pnie_bincont <- exp((theta2*beta1+theta3*beta1*astar)*(a-astar))
tnie_bincont <- exp((theta2*beta1+theta3*beta1*a)*(a-astar))
te_bincont <- pnde_bincont*tnie_bincont
cde_err_bincont <- (exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
exp(theta2*m-(theta2+theta3*astar)*(beta0+beta1*astar+beta2*meanc1+beta3*meanc2)-0.5*(theta2+theta3*astar)^2*s^2)
intref_err_bincont <- pnde_bincont - 1 - cde_err_bincont
intmed_err_bincont <- tnie_bincont * pnde_bincont - pnde_bincont - pnie_bincont + 1
pnie_err_bincont <- pnie_bincont - 1
total_err_bincont <- te_bincont - 1
cde_err_prop_bincont <- cde_err_bincont/total_err_bincont
intmed_err_prop_bincont <- intmed_err_bincont/total_err_bincont
intref_err_prop_bincont <- intref_err_bincont/total_err_bincont
pnie_err_prop_bincont <- pnie_err_bincont/total_err_bincont
pm_bincont <- (pnie_err_bincont+intmed_err_bincont)/total_err_bincont
int_bincont <- (intref_err_bincont+intmed_err_bincont)/total_err_bincont
pe_bincont <- (intref_err_bincont+intmed_err_bincont+pnie_err_bincont)/total_err_bincont
ref <- c(cde_bincont, pnde_bincont, tnde_bincont, pnie_bincont, tnie_bincont, te_bincont, cde_err_bincont,
intref_err_bincont, intmed_err_bincont, pnie_err_bincont, cde_err_prop_bincont,
intref_err_prop_bincont, intmed_err_prop_bincont, pnie_err_prop_bincont,
pm_bincont, int_bincont, pe_bincont)
# test
expect_equal(unname(res_bincont$effect.pe), ref)
})
test_that("cmest works correctly for continuous Y and continuous M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rnorm(n, 1 + 2*A + 1.5*C1 + 0.8*C2, 1)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
# results of cmest
res_contcont <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("linear"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
# reference results
yreg <- lm(Y ~ A*M + C1 + C2, data = data)
mreg <- lm(M ~ A + C1 + C2, data = data)
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_contcont <- (theta1+theta3*m)*(a-astar)
pnde_contcont <- (theta1+theta3*(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(a-astar)
tnde_contcont <- (theta1+theta3*(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(a-astar)
pnie_contcont <- (theta2*beta1+theta3*beta1*astar)*(a-astar)
tnie_contcont <- (theta2*beta1+theta3*beta1*a)*(a-astar)
te_contcont <- pnde_contcont+tnie_contcont
intref_contcont <- pnde_contcont-cde_contcont
intmed_contcont <- tnie_contcont-pnie_contcont
cde_prop_contcont <- cde_contcont/te_contcont
intref_prop_contcont <- intref_contcont/te_contcont
intmed_prop_contcont <- intmed_contcont/te_contcont
pnie_prop_contcont <- pnie_contcont/te_contcont
pm_contcont <- (pnie_contcont+intmed_contcont)/te_contcont
int_contcont <- (intref_contcont+intmed_contcont)/te_contcont
pe_contcont <- (intref_contcont+intmed_contcont+pnie_contcont)/te_contcont
ref <- c(cde_contcont, pnde_contcont, tnde_contcont, pnie_contcont, tnie_contcont, te_contcont,
intref_contcont, intmed_contcont, cde_prop_contcont, intref_prop_contcont, intmed_prop_contcont,
pnie_prop_contcont, pm_contcont, int_contcont, pe_contcont)
# test
expect_equal(unname(res_contcont$effect.pe), ref)
})
test_that("cmest works correctly for continuous Y and binary M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
yreg <- lm(Y ~ A*M + C1 + C2, data = data)
mreg <- glm(M ~ A + C1 + C2, family = binomial, data = data)
# results of cmest
res_contbin_rb_param_delta <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
res_contbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
# mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
# mreg = list("multinomial"), yreg = "linear",
# astar = 0, a = 1, mval = list(1),
# estimation = "imputation", inference = "bootstrap")
res_contbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_contbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# reference results
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_contbin <- (theta1+theta3*m)*(a-astar)
pnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2)
tnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)
pnie_contbin <- (theta2+theta3*astar)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
tnie_contbin <- (theta2+theta3*a)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
te_contbin <- pnde_contbin+tnie_contbin
intref_contbin <- pnde_contbin-cde_contbin
intmed_contbin <- tnie_contbin-pnie_contbin
cde_prop_contbin <- cde_contbin/te_contbin
intref_prop_contbin <- intref_contbin/te_contbin
intmed_prop_contbin <- intmed_contbin/te_contbin
pnie_prop_contbin <- pnie_contbin/te_contbin
pm_contbin<- (pnie_contbin+intmed_contbin)/te_contbin
int_contbin <- (intref_contbin+intmed_contbin)/te_contbin
pe_contbin <- (intref_contbin+intmed_contbin+pnie_contbin)/te_contbin
ref <- c(cde_contbin, pnde_contbin, tnde_contbin, pnie_contbin, tnie_contbin, te_contbin,
intref_contbin, intmed_contbin, cde_prop_contbin, intref_prop_contbin, intmed_prop_contbin,
pnie_prop_contbin, pm_contbin, int_contbin, pe_contbin)
# test
expect_equal(unname(res_contbin_rb_param_delta$effect.pe), ref)
expect_equal(unname(res_contbin_rb_param_bootstrap$effect.pe), ref)
expect_equal(unname(res_contbin_rb_impu_bootstrap$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_wb$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_msm$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_ne$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_iorw$effect.pe), ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(unname(res_contbin_gformula$effect.pe), ref, tolerance = 0.1)
})
test_that("cmest works correctly for multiple imputations", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
yreg <- lm(Y ~ A*M + C1 + C2, data = data)
mreg <- glm(M ~ A + C1 + C2, family = binomial, data = data)
# results of cmest
res_contbin_rb_param_delta <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta", multimp = TRUE)
res_contbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap", multimp = TRUE)
res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_contbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
# reference results
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_contbin <- (theta1+theta3*m)*(a-astar)
pnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2)
tnde_contbin <- theta1*(a-astar) + theta3*(a-astar)*expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)
pnie_contbin <- (theta2+theta3*astar)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
tnie_contbin <- (theta2+theta3*a)*(expit(beta0+beta1*a+beta2*meanc1+beta3*meanc2)-expit(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))
te_contbin <- pnde_contbin+tnie_contbin
intref_contbin <- pnde_contbin-cde_contbin
intmed_contbin <- tnie_contbin-pnie_contbin
cde_prop_contbin <- cde_contbin/te_contbin
intref_prop_contbin <- intref_contbin/te_contbin
intmed_prop_contbin <- intmed_contbin/te_contbin
pnie_prop_contbin <- pnie_contbin/te_contbin
pm_contbin<- (pnie_contbin+intmed_contbin)/te_contbin
int_contbin <- (intref_contbin+intmed_contbin)/te_contbin
pe_contbin <- (intref_contbin+intmed_contbin+pnie_contbin)/te_contbin
ref <- c(cde_contbin, pnde_contbin, tnde_contbin, pnie_contbin, tnie_contbin, te_contbin,
intref_contbin, intmed_contbin, cde_prop_contbin, intref_prop_contbin, intmed_prop_contbin,
pnie_prop_contbin, pm_contbin, int_contbin, pe_contbin)
# test
expect_equal(unname(res_contbin_rb_param_delta$effect.pe), ref)
expect_equal(unname(res_contbin_rb_param_bootstrap$effect.pe), ref)
expect_equal(unname(res_contbin_rb_impu_bootstrap$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_wb$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_msm$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_ne$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_contbin_iorw$effect.pe), ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(unname(res_contbin_gformula$effect.pe), ref, tolerance = 0.1)
})
test_that("cmest works correctly for bca", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
Y <- rnorm(n, -1 + 0.8*A + 0.5*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 1)
data <- data.frame(A, M, Y, C1, C2)
# results of cmest
res_contbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
res_contbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap",
boot.ci.type = "bca")
# test
ref <- summary(res_contbin_rb_param_bootstrap)$summarydf$Estimate
expect_equal(summary(res_contbin_rb_impu_bootstrap)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_wb)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_msm)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_ne)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_contbin_iorw)$summarydf$Estimate, ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(summary(res_contbin_gformula)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(class(print(res_contbin_rb_param_bootstrap)), "list")
expect_equal(class(print(res_contbin_rb_impu_bootstrap)), "list")
expect_equal(class(print(res_contbin_wb)), "list")
expect_equal(class(print(res_contbin_msm)), "list")
expect_equal(class(print(res_contbin_ne)), "list")
expect_equal(class(print(res_contbin_iorw)), "list")
expect_equal(class(print(res_contbin_gformula)), "list")
expect_equal(class(print(summary(res_contbin_rb_impu_bootstrap))), "list")
expect_equal(class(print(summary(res_contbin_wb))), "list")
expect_equal(class(print(summary(res_contbin_msm))), "list")
expect_equal(class(print(summary(res_contbin_ne))), "list")
expect_equal(class(print(summary(res_contbin_iorw))), "list")
expect_equal(class(print(summary(res_contbin_gformula))), "list")
})
test_that("cmest works correctly for survival Y and count M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 - 0.3*C2)
A <- rbinom(n, 1, pa)
M <- rpois(n, exp(1 + 0.2*A + 0.2*C1 + 0.1*C2))
Y <- rexp(n,exp(-1 - 0.3*A - 0.1*M - 0.1*A*M + 0.3*C1 - 0.6*C2))
cen <- quantile(Y, probs = 0.01)
delta <- as.numeric(Y > cen)
data <- data.frame(A, M, Y, C1, C2, delta)
ereg <- glm(A ~ C1 + C2, family = binomial(), data = data)
yreg <- survival::survreg(survival::Surv(Y, delta) ~ A*M + C1 + C2, data = data, dist = "weibull")
mreg <- glm(M ~ A + C1 + C2, family = poisson(), data = data)
# results of cmest
# res_survcount_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
# exposure = "A", mediator = "M", basec = c("C1", "C2"),
# EMint = TRUE,
# mreg = list("linear"), yreg = "aft_exp",
# astar = 0, a = 1, mval = list(1),
# estimation = "paramfunc", inference = "delta")
res_survcount_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("poisson"), yreg = "aft_weibull",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survcount_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
ereg = "logistic", yreg = "aft_weibull",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_survcount_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
mreg = list("poisson"), yreg = "aft_weibull",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_survcount_rb$effect.pe)[c(6,2,5,15)],
unname(res_survcount_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survcount_rb$effect.pe),
unname(res_survcount_gformula$effect.pe), tolerance = 0.1)
# results of cmest
res_survcount_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("negbin"), yreg = "coxph",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survcount_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
ereg = "logistic", yreg = "coxph",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_survcount_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
mreg = list("negbin"), yreg = "coxph",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_survcount_rb$effect.pe)[c(6,2,5,15)],
unname(res_survcount_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survcount_rb$effect.pe),
unname(res_survcount_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for count Y and count M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 - 0.3*C2)
A <- rbinom(n, 1, pa)
M <- rpois(n, exp(1 + 0.2*A + 0.2*C1 + 0.1*C2))
Y <- rpois(n,exp(-1 - 0.3*A - 0.1*M - 0.1*A*M + 0.3*C1 - 0.6*C2))
data <- data.frame(A, M, Y, C1, C2)
ereg <- glm(A ~ C1 + C2, family = binomial(), data = data)
yreg <- glm(Y ~ A*M + C1 + C2, family = quasipoisson(), data = data)
mreg <- glm(M ~ A + C1 + C2, family = quasipoisson(), data = data)
# results of cmest
res_countcount_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("quasipoisson"), yreg = "quasipoisson",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_countcount_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "quasipoisson",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_countcount_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "quasipoisson",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_countcount_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("quasipoisson"), yreg = "quasipoisson",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_countcount_rb$effect.pe),
unname(res_countcount_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_countcount_rb$effect.pe)[c(6,2,5,15)],
unname(res_countcount_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_countcount_rb$effect.pe),
unname(res_countcount_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for survival Y and ordinal M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
proba1to2 <- expit(0.4 + 0.5*C1 - 0.3*C2)
proba2 <- expit(-0.8 + 0.5*C1 - 0.3*C2)
proba0 <- 1 - proba1to2
proba1 <- proba1to2 - proba2
A <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(proba0[x],
proba1[x],
proba2[x])))
A <- as.factor(A)
probm1to2 <- expit(0.5 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm2 <- expit(-1 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm0 <- 1 - probm1to2
probm1 <- probm1to2 - probm2
M <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(probm0[x],
probm1[x],
probm2[x])))
M <- as.factor(M)
Y <- rexp(n,exp(-1 - 0.3*(A == 1) - 0.1*(A == 2) - 0.1*(M == 1) - 0.3*(M == 2) -
0.1*(A == 1)*(M == 1) - 0.3*(A == 1)*(M == 2) -
0.2*(A == 2)*(M == 1) - 0.1*(A == 2)*(M == 2) +
0.3*C1 - 0.6*C2))
cen <- quantile(Y, probs = 0.01)
delta <- as.numeric(Y > cen)
data <- data.frame(A, M, Y, C1, C2, delta)
ereg <- MASS::polr(A ~ C1 + C2, data = data)
yreg <- survival::survreg(survival::Surv(Y, delta) ~ A*M + C1 + C2, data = data, dist = "exponential")
mreg <- MASS::polr(M ~ A + C1 + C2, data = data)
# results of cmest
res_survordinal_rb <- cmest(data = data, model = "rb", outcome = "Y", event = "delta",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("ordinal"), yreg = "aft_exp",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survordinal_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
ereg = "ordinal", yreg = "aft_exp",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_survordinal_msm <- cmest(data = data, model = "msm",
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "ordinal", yreg = "aft_exp", mreg = list("ordinal"),
wmnomreg = list("ordinal"), wmdenomreg = list("ordinal"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_survordinal_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE, event = "delta",
mreg = list("ordinal"), yreg = "aft_exp",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_survordinal_rb$effect.pe)[c(6,2,5,15)],
unname(res_survordinal_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survordinal_rb$effect.pe),
unname(res_survordinal_msm$effect.pe), tolerance = 0.1)
expect_equal(unname(res_survordinal_rb$effect.pe),
unname(res_survordinal_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for ordinal Y and ordinal M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 1)
C2 <- rbinom(n, 1, 0.6)
proba1to2 <- expit(0.4 + 0.5*C1 - 0.3*C2)
proba2 <- expit(-0.8 + 0.5*C1 - 0.3*C2)
proba0 <- 1 - proba1to2
proba1 <- proba1to2 - proba2
A <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(proba0[x],
proba1[x],
proba2[x])))
A <- as.factor(A)
probm1to2 <- expit(0.5 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm2 <- expit(-1 + 0.2*(A == 1) + 0.1*(A == 2) + 0.2*C1 + 0.1*C2)
probm0 <- 1 - probm1to2
probm1 <- probm1to2 - probm2
M <- sapply(1:n, FUN = function(x) sample(c(0,1,2),size=1,replace=TRUE,
prob=c(probm0[x],
probm1[x],
probm2[x])))
M <- as.factor(M)
proby1to2 <- expit(1 - 0.3*(A == 1) - 0.1*(A == 2) - 0.1*(M == 1) - 0.3*(M == 2) -
0.1*(A == 1)*(M == 1) - 0.3*(A == 1)*(M == 2) -
0.2*(A == 2)*(M == 1) - 0.1*(A == 2)*(M == 2) +
0.3*C1 - 0.6*C2)
proby2 <- expit(-0.5 - 0.3*(A == 1) - 0.1*(A == 2) - 0.1*(M == 1) - 0.3*(M == 2) -
0.1*(A == 1)*(M == 1) - 0.3*(A == 1)*(M == 2) -
0.2*(A == 2)*(M == 1) - 0.1*(A == 2)*(M == 2) +
0.3*C1 - 0.6*C2)
proby0 <- 1 - proby1to2
proby1 <- proby1to2 - proby2
Y <- sapply(1:n, FUN = function(x) sample(c(0,1,2), size = 1, replace = TRUE,
prob=c(proby0[x],
proby1[x],
proby2[x])))
Y <- as.factor(Y)
data <- data.frame(A, M, Y, C1, C2)
ereg <- MASS::polr(A ~ C1 + C2, data = data)
yreg <- MASS::polr(Y ~ A*M + C1 + C2, data = data)
mreg <- MASS::polr(M ~ A + C1 + C2, data = data)
# results of cmest
res_ordinalordinal_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("ordinal"), yreg = "ordinal", yval = "1",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_ordinalordinal_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "ordinal", yreg = "ordinal", yval = "1",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_ordinalordinal_msm <- cmest(data = data, model = "msm",
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "ordinal", yreg = "ordinal", mreg = list("ordinal"),
wmnomreg = list("ordinal"), wmdenomreg = list("ordinal"),
astar = 0, a = 1, mval = list(1), yval = "1",
estimation = "imputation", inference = "bootstrap")
res_ordinalordinal_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("ordinal"), yreg = "ordinal",
astar = 0, a = 1, mval = list(1), yval = "1",
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_ordinalordinal_rb$effect.pe)[c(6,2,5)],
unname(res_ordinalordinal_iorw$effect.pe)[1:3], tolerance = 0.1)
expect_equal(unname(res_ordinalordinal_rb$effect.pe),
unname(res_ordinalordinal_msm$effect.pe), tolerance = 0.1)
expect_equal(unname(res_ordinalordinal_rb$effect.pe),
unname(res_ordinalordinal_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for continuous Y and gamma M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rgamma(n, shape = 2, scale = exp(-3 + 2*A + 1.5*C1 + 0.8*C2)/2)
Y <- rnorm(n, -3 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2, 0.5)
data <- data.frame(A, M, Y, C1, C2)
mreg <- glm(M ~ A + C1 + C2, data = data, family = Gamma("log"))
# results of cmest
res_gammalinear_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
mreg = list(mreg), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_gammalinear_wb <- cmest(data = data, model = "wb", outcome = "Y",
exposure = "A", mediator = "M", basec = c("C1", "C2"),
EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_gammalinear_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_gammalinear_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_gammalinear_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list(mreg), yreg = "linear",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_gammalinear_rb$effect.pe),
unname(res_gammalinear_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_gammalinear_rb$effect.pe)[c(6,2,5,15)],
unname(res_gammalinear_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_gammalinear_rb$effect.pe),
unname(res_gammalinear_ne$effect.pe), tolerance = 0.1)
expect_equal(unname(res_gammalinear_rb$effect.pe),
unname(res_gammalinear_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for multiple mediators", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M1 <- rbinom(n, 1, expit(1 + 2*A + 1.5*C1 + 0.8*C2))
M2 <- rpois(n, exp(1 + 0.2*A + 0.3*M1 + 0.2*C1 + 0.1*C2))
Y <- rnorm(n, -3 + 0.8*A - 1.8*M1 + 0.6*M2 + 0.5*A*M1 + 0.8*A*M2 + 0.3*C1 - 0.6*C2, 0.5)
data <- data.frame(A, M1, M2, Y, C1, C2)
# results of cmest
res_multipleM_rb <- cmest(data = data, model = "rb", outcome = "Y",
exposure = "A", mediator = c("M1", "M2"), basec = c("C1", "C2"),
EMint = TRUE,
mreg = list("logistic", "poisson"), yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
res_multipleM_wb <- cmest(data = data, model = "wb", outcome = "Y",
exposure = "A", mediator = c("M1", "M2"), basec = c("C1", "C2"),
EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
res_multipleM_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "linear",
astar = 0, a = 1,
estimation = "imputation", inference = "bootstrap")
res_multipleM_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
res_multipleM_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic", "poisson"), yreg = "linear",
astar = 0, a = 1, mval = list(1, 5),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_multipleM_rb$effect.pe),
unname(res_multipleM_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_multipleM_rb$effect.pe)[c(6,2,5,15)],
unname(res_multipleM_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_multipleM_rb$effect.pe),
unname(res_multipleM_ne$effect.pe), tolerance = 0.1)
expect_equal(unname(res_multipleM_rb$effect.pe),
unname(res_multipleM_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for binary Y and binary M with postc", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
L <- rnorm(n, mean = 0.5 - A + 0.2*C1 + 0.6*C2)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2 + L)
M <- rbinom(n, 1, pm)
py <- expit(-3 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2 + L)
Y <- rbinom(n, 1, py)
data <- data.frame(A, M, Y, C1, C2, L)
# results of cmest
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
expect_error(cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
res_binbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
expect_error(cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap"),
"When postc is not empty, select model from 'msm' and 'gformula'")
res_binbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic", postcreg = list("linear"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(summary(res_binbin_gformula)$summarydf$Estimate,
summary(res_binbin_msm)$summarydf$Estimate, tolerance = 0.1)
expect_equal(class(print(res_binbin_msm)), "list")
expect_equal(class(print(res_binbin_gformula)), "list")
expect_equal(class(print(summary(res_binbin_msm))), "list")
expect_equal(class(print(summary(res_binbin_gformula))), "list")
res_binbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
res_binbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), postc = "L", EMint = TRUE,
mreg = list("logistic"), yreg = "logistic", postcreg = list("linear"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap", multimp = TRUE)
# test
expect_equal(summary(res_binbin_gformula)$summarydf$Estimate,
summary(res_binbin_msm)$summarydf$Estimate, tolerance = 0.1)
expect_equal(class(print(res_binbin_msm)), "list")
expect_equal(class(print(res_binbin_gformula)), "list")
expect_equal(class(print(summary(res_binbin_msm))), "list")
expect_equal(class(print(summary(res_binbin_gformula))), "list")
})
test_that("cmest works correctly for regressions with prior weights", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 1000000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M1 <- rbinom(n, 1, expit(0.5 + 0.2*A + 0.5*C1 - 0.8*C2))
sum(M1)
M2 <- rbinom(n, 1, expit(-1 + A + M1 - 0.5*C1 + 0.5*C2))
sum(M2)
py <- expit(-6 + 0.8*A - 1.2*M1 + 0.5*M2 + 0.3*A*M1 + 0.2*A*M2 + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
yprevalence <- sum(Y)/n
data <- data.frame(A, M1, M2, Y, C1, C2)
case_indice <- sample(which(data$Y == 1), 2000, replace = FALSE)
control_indice <- sample(which(data$Y == 0), 2000, replace = FALSE)
data <- data[c(case_indice, control_indice), ]
mreg1 <- glm(M1 ~ A + C1 + C2, data = data, family = binomial, weights = rep(2, 4000))
mreg2 <- glm(M2 ~ A + C1 + C2, data = data, family = binomial, weights = rep(2, 4000))
mreg1_msm <- glm(M1 ~ A, data = data, family = binomial, weights = rep(2, 4000))
mreg2_msm <- glm(M2 ~ A, data = data, family = binomial, weights = rep(2, 4000))
# yprevalence = yprevalence
# results of cmest
res_binbin_rb_impu_bootstrap <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
mreg = list(mreg1, mreg2), yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_wb <- cmest(data = data, model = "wb", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_iorw <- cmest(data = data, model = "iorw", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_msm <- cmest(data = data, model = "msm", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list(mreg1_msm, mreg2_msm),
wmnomreg = list("logistic", "logistic"), wmdenomreg = list("logistic", "logistic"),
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_ne <- cmest(data = data, model = "ne", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
res_binbin_gformula <- cmest(data = data, model = "gformula", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = c("M1", "M2"), basec = c("C1", "C2"), EMint = TRUE,
mreg = list(mreg1, mreg2), yreg = "logistic",
astar = 0, a = 1, mval = list(1, 1),
estimation = "imputation", inference = "bootstrap")
# test
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_wb$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe)[c(6,2,5,15)],
unname(res_binbin_iorw$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_ne$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_msm$effect.pe), tolerance = 0.1)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe),
unname(res_binbin_gformula$effect.pe), tolerance = 0.1)
})
test_that("cmest works correctly for binary Y and binary M", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
pm <- expit(1 + 2*A + 1.5*C1 + 0.8*C2)
M <- rbinom(n, 1, pm)
py <- expit(-3 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
data <- data.frame(A, M, Y, C1, C2)
yreg <- glm(Y ~ A*M + C1 + C2, family = binomial(), data = data)
mreg <- glm(M ~ A + C1 + C2, family = binomial(), data = data)
# results of cmest
res_binbin_rb_param_delta <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "para", inference = "delt")
res_binbin_rb_param_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
res_binbin_rb_impu_bootstrap <- cmest(data = data, model = "rb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "impu", inference = "boot")
res_binbin_wb <- cmest(data = data, model = "wb", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_iorw <- cmest(data = data, model = "iorw", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_msm <- cmest(data = data, model = "msm", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmdenomreg = list("logistic"), wmnomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_ne <- cmest(data = data, model = "ne", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_gformula <- cmest(data = data, model = "gformula", outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# reference results
thetas <- unname(coef(yreg))
betas <- unname(coef(mreg))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(C1)
meanc2 <- mean(C2)
cde_binbin <- exp((theta1+theta3*m)*(a-astar))
pnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))
pnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
te_binbin <- pnde_binbin*tnie_binbin
cde_err_binbin <- exp(theta2*m)*(exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))/
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2+theta3*astar))
intref_err_binbin <- pnde_binbin - 1 - cde_err_binbin
intmed_err_binbin <- tnie_binbin * pnde_binbin - pnde_binbin - pnie_binbin + 1
pnie_err_binbin <- pnie_binbin - 1
total_err_binbin <- te_binbin - 1
cde_err_prop_binbin <- cde_err_binbin/total_err_binbin
intmed_err_prop_binbin <- intmed_err_binbin/total_err_binbin
intref_err_prop_binbin <- intref_err_binbin/total_err_binbin
pnie_err_prop_binbin <- pnie_err_binbin/total_err_binbin
pm_binbin <- (pnie_err_binbin+intmed_err_binbin)/total_err_binbin
int_binbin <- (intref_err_binbin+intmed_err_binbin)/total_err_binbin
pe_binbin <- (intref_err_binbin+intmed_err_binbin+pnie_err_binbin)/total_err_binbin
ref <- c(cde_binbin, pnde_binbin, tnde_binbin, pnie_binbin, tnie_binbin, te_binbin, cde_err_binbin,
intref_err_binbin, intmed_err_binbin, pnie_err_binbin, cde_err_prop_binbin,
intref_err_prop_binbin, intmed_err_prop_binbin, pnie_err_prop_binbin,
pm_binbin, int_binbin, pe_binbin)
# test
expect_equal(summary(res_binbin_rb_param_delta)$summarydf$Estimate, ref)
expect_equal(class(ggcmest(res_binbin_rb_param_delta)), c("gg", "ggplot"))
expect_equal(summary(res_binbin_rb_param_bootstrap)$summarydf$Estimate, ref)
expect_equal(summary(res_binbin_rb_impu_bootstrap)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_wb)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_msm)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_ne)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(summary(res_binbin_iorw)$summarydf$Estimate, ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(summary(res_binbin_gformula)$summarydf$Estimate, ref, tolerance = 0.1)
expect_equal(class(print(res_binbin_rb_param_delta)), "list")
expect_equal(class(print(res_binbin_rb_param_bootstrap)), "list")
expect_equal(class(print(res_binbin_rb_impu_bootstrap)), "list")
expect_equal(class(print(res_binbin_wb)), "list")
expect_equal(class(print(res_binbin_msm)), "list")
expect_equal(class(print(res_binbin_ne)), "list")
expect_equal(class(print(res_binbin_iorw)), "list")
expect_equal(class(print(res_binbin_gformula)), "list")
expect_equal(class(print(summary(res_binbin_rb_param_delta))), "list")
expect_equal(class(print(summary(res_binbin_rb_param_bootstrap))), "list")
expect_equal(class(print(summary(res_binbin_rb_impu_bootstrap))), "list")
expect_equal(class(print(summary(res_binbin_wb))), "list")
expect_equal(class(print(summary(res_binbin_msm))), "list")
expect_equal(class(print(summary(res_binbin_ne))), "list")
expect_equal(class(print(summary(res_binbin_iorw))), "list")
expect_equal(class(print(summary(res_binbin_gformula))), "list")
})
test_that("cmest works correctly for binary Y and binary M in a case control study", {
set.seed(1)
# data simulation
expit <- function(x) exp(x)/(1+exp(x))
n <- 10000000
C1 <- rnorm(n, mean = 1, sd = 0.1)
C2 <- rbinom(n, 1, 0.6)
pa <- expit(0.2 + 0.5*C1 + 0.1*C2)
A <- rbinom(n, 1, pa)
M <- rbinom(n, 1, expit(1 + 2*A + 1.5*C1 + 0.8*C2))
py <- expit(-5 + 0.8*A - 1.8*M + 0.5*A*M + 0.3*C1 - 0.6*C2)
Y <- rbinom(n, 1, py)
yprevalence <- sum(Y)/n
data <- data.frame(A, M, Y, C1, C2)
case_indice <- sample(which(data$Y == 1), 5000, replace = FALSE)
control_indice <- sample(which(data$Y == 0), 5000, replace = FALSE)
data <- data[c(case_indice, control_indice), ]
# yrare = TRUE
# results of cmest
res_binbin_rb_param_delta <- cmest(data = data, model = "rb", casecontrol = TRUE, yrare = TRUE,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
res_binbin_rb_param_bootstrap <- cmest(data = data, model = "rb", casecontrol = TRUE, yrare = TRUE,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
# reference results
thetas <- unname(coef(res_binbin_rb_param_delta$reg.output$yreg))
betas <- unname(coef(res_binbin_rb_param_delta$reg.output$mreg[[1]]))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(data$C1)
meanc2 <- mean(data$C2)
cde_binbin <- exp((theta1+theta3*m)*(a-astar))
pnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))
pnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
te_binbin <- pnde_binbin*tnie_binbin
cde_err_binbin <- exp(theta2*m)*(exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))/
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2+theta3*astar))
intref_err_binbin <- pnde_binbin - 1 - cde_err_binbin
intmed_err_binbin <- tnie_binbin * pnde_binbin - pnde_binbin - pnie_binbin + 1
pnie_err_binbin <- pnie_binbin - 1
total_err_binbin <- te_binbin - 1
cde_err_prop_binbin <- cde_err_binbin/total_err_binbin
intmed_err_prop_binbin <- intmed_err_binbin/total_err_binbin
intref_err_prop_binbin <- intref_err_binbin/total_err_binbin
pnie_err_prop_binbin <- pnie_err_binbin/total_err_binbin
pm_binbin <- (pnie_err_binbin+intmed_err_binbin)/total_err_binbin
int_binbin <- (intref_err_binbin+intmed_err_binbin)/total_err_binbin
pe_binbin <- (intref_err_binbin+intmed_err_binbin+pnie_err_binbin)/total_err_binbin
ref <- c(cde_binbin, pnde_binbin, tnde_binbin, pnie_binbin, tnie_binbin, te_binbin, cde_err_binbin,
intref_err_binbin, intmed_err_binbin, pnie_err_binbin, cde_err_prop_binbin,
intref_err_prop_binbin, intmed_err_prop_binbin, pnie_err_prop_binbin,
pm_binbin, int_binbin, pe_binbin)
# test
expect_equal(unname(res_binbin_rb_param_delta$effect.pe), ref)
expect_equal(unname(res_binbin_rb_param_bootstrap$effect.pe), ref)
# yprevalence = yprevalence
# results of cmest
res_binbin_rb_param_delta <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "loglinear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "delta")
res_binbin_rb_param_bootstrap <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "loglinear",
astar = 0, a = 1, mval = list(1),
estimation = "paramfunc", inference = "bootstrap")
res_binbin_rb_impu_bootstrap <- cmest(data = data, model = "rb",
casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_wb <- cmest(data = data, model = "wb", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_iorw <- cmest(data = data, model = "iorw", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_msm <- cmest(data = data, model = "msm", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
ereg = "logistic", yreg = "logistic", mreg = list("logistic"),
wmnomreg = list("logistic"), wmdenomreg = list("logistic"),
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_ne <- cmest(data = data, model = "ne", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
res_binbin_gformula <- cmest(data = data, model = "gformula", casecontrol = TRUE, yprevalence = yprevalence,
outcome = "Y", exposure = "A",
mediator = "M", basec = c("C1", "C2"), EMint = TRUE,
mreg = list("logistic"), yreg = "logistic",
astar = 0, a = 1, mval = list(1),
estimation = "imputation", inference = "bootstrap")
# reference results
thetas <- unname(coef(res_binbin_rb_param_delta$reg.output$yreg))
betas <- unname(coef(res_binbin_rb_param_delta$reg.output$mreg[[1]]))
beta0 <- betas[1]
beta1 <- betas[2]
beta2 <- betas[3]
beta3 <- betas[4]
theta0 <- thetas[1]
theta1 <- thetas[2]
theta2 <- thetas[3]
theta3 <- thetas[6]
theta4 <- thetas[4]
theta5 <- thetas[5]
m <- 1
a <- 1
astar <- 0
meanc1 <- mean(data$C1)
meanc2 <- mean(data$C2)
cde_binbin <- exp((theta1+theta3*m)*(a-astar))
pnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnde_binbin <- (exp(theta1*a)*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
(exp(theta1*astar)*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))
pnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*astar+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
tnie_binbin <- ((1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*a+beta2*meanc1+beta3*meanc2)))/
((1+exp(beta0+beta1*a+beta2*meanc1+beta3*meanc2))*(1+exp(theta2+theta3*a+beta0+beta1*astar+beta2*meanc1+beta3*meanc2)))
te_binbin <- pnde_binbin*tnie_binbin
cde_err_binbin <- exp(theta2*m)*(exp(theta1*(a-astar)+theta3*a*m)-exp(theta3*astar*m))*
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2))/
(1+exp(beta0+beta1*astar+beta2*meanc1+beta3*meanc2+theta2+theta3*astar))
intref_err_binbin <- pnde_binbin - 1 - cde_err_binbin
intmed_err_binbin <- tnie_binbin * pnde_binbin - pnde_binbin - pnie_binbin + 1
pnie_err_binbin <- pnie_binbin - 1
total_err_binbin <- te_binbin - 1
cde_err_prop_binbin <- cde_err_binbin/total_err_binbin
intmed_err_prop_binbin <- intmed_err_binbin/total_err_binbin
intref_err_prop_binbin <- intref_err_binbin/total_err_binbin
pnie_err_prop_binbin <- pnie_err_binbin/total_err_binbin
pm_binbin <- (pnie_err_binbin+intmed_err_binbin)/total_err_binbin
int_binbin <- (intref_err_binbin+intmed_err_binbin)/total_err_binbin
pe_binbin <- (intref_err_binbin+intmed_err_binbin+pnie_err_binbin)/total_err_binbin
ref <- c(cde_binbin, pnde_binbin, tnde_binbin, pnie_binbin, tnie_binbin, te_binbin, cde_err_binbin,
intref_err_binbin, intmed_err_binbin, pnie_err_binbin, cde_err_prop_binbin,
intref_err_prop_binbin, intmed_err_prop_binbin, pnie_err_prop_binbin,
pm_binbin, int_binbin, pe_binbin)
# test
expect_equal(unname(res_binbin_rb_param_delta$effect.pe), ref)
#expect_equal(unname(res_binbin_rb_param_delta_multinom$effect.pe), ref, tolerance = 0.001)
expect_equal(class(print(res_binbin_rb_param_delta)), "list")
#expect_equal(class(print(res_binbin_rb_param_delta_multinom)), "list")
expect_equal(unname(res_binbin_rb_param_bootstrap$effect.pe), ref)
expect_equal(unname(res_binbin_rb_impu_bootstrap$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_wb$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_msm$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_ne$effect.pe), ref, tolerance = 0.1)
expect_equal(unname(res_binbin_iorw$effect.pe), ref[c(6,2,5,15)], tolerance = 0.1)
expect_equal(unname(res_binbin_gformula$effect.pe), ref, tolerance = 0.1)
#expect_equal(class(print(summary(res_binbin_rb_param_delta_multinom))), "list")
})
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