tests/testthat/test-multistate.R
In LindaValeri/CMAverse: Causal Mediation Analysis
context("cmest_multistate works correctly")
test_that("cmest_multistate correctly estimates RD and SD for a multicategorical exposure", {
# generate data
# set up coefficients
# M (trans 1)
a1 = -1.9
a2 = 0.2
a3 = 0.5
# S (trans 2)
b1 = 1
b3 = -0.5
b4 = 0.3
# M when generating the semi-competing observations in resample() (trans 3)
# S (resample) (trans 3)
c1 = 0.55
c2 = -0.15
c3 = -0.1
c4 = -0.2
# generate dataset
set.seed(8)
# build a function to generate time-to-event data
gen_srv <- function(n, lambda, beta, X){
X = as.matrix(X)
beta = as.matrix(beta, ncol=1)
time = -log(runif(n)) / (lambda * exp(X %*% beta)) # exponential distribution
return(time)
}
n <- 1000
A = sample(c(1,2,3),replace=TRUE, size=n, c(0.2,0.3,0.5)) #multi-categorical exposure
C1 = sample(c(0,1),replace=TRUE, size=n,c(0.6, 0.4)) #binary confounder
C2 = rnorm(n, mean = 1, sd = 1) #continuous confounder
id=c(1:n)
full = data.frame(id,A,C1,C2)
M = gen_srv(n=n, lambda = 1.5, beta = c(a1,a2,a3), X=data.frame(A,C1,C2)) #time to event mediator
S = gen_srv(n=n, lambda = 1, beta = c(b1,b3,b4), X=data.frame(A,C1,C2)) #time to event outcome
data = data.frame(id = c(1:n), M = M, S = S)
# indicator for event
data$ind_M = ifelse(data$M <= data$S, 1, 0)
data$ind_S = 1
data <- merge(data,full , by = "id")
#modify Y distribution
trans_matrix = mstate::transMat(x = list(c(2, 3), c(3), c()), names = c("A", "M", "S"))
covs = c("A","M", "C1","C2")
pre_data = mstate::msprep(time = c(NA, "M", "S"), status = c(NA, "ind_M", "ind_S"),
data = data, trans = trans_matrix, keep = covs)
pre_data = mstate::expand.covs(pre_data, covs, append = TRUE, longnames = FALSE)
#pre_data$A_M.3 = pre_data$A.3*pre_data$M.3
# resample for T < S
data_23 = pre_data[which(pre_data$trans == 3),]
data_23_tem = data.frame(id = rep(NA,dim(data_23)[1]),
new_y = rep(NA,dim(data_23)[1]))
paste("# to resample is ", nrow(data_23))
for(i in 1:dim(data_23)[1]){
data_23_tem$id[i] = data_23$id[i]
repeat {
time_test = gen_srv(n = 1,
lambda = 0.8,
beta = c(as.numeric(c1),
c2,
as.numeric(c3),
as.numeric(c4)),
X = data_23[i, c("A.3", "M.3", "C1.3","C2.3")])
# exit if the condition is met
if (time_test > data_23[i,"M.3"]) break
}
data_23_tem$new_y[i] = time_test
}
data_temp = merge(data, data_23_tem, by = "id", all = T)
# modify Y and M
data_temp$S[which(data_temp$ind_M == 1)] = data_temp$new_y[which(data_temp$ind_M == 1)]
data_temp$M[which(data_temp$ind_M == 0)] = data_temp$S[which(data_temp$ind_M == 0)]
data_final = data_temp
data_final$A = as.factor(data_final$A) #generate a factor exposure
sc_data = data_final %>% dplyr::select(id,A,M,S,ind_M,ind_S,C1,C2)
# generate time to censoring C; compare C to S; update event indicator
time_to_censor = runif(n, 0, 2*max(sc_data$S))
sc_data$ind_S = ifelse(sc_data$S > time_to_censor, 0, 1)
sc_data$A = factor(sc_data$A)
sc_data$C1 = factor(sc_data$C1)
print(summary(sc_data$S))
# get cmest_multistate estimate
time_to_predict_sc = c(0.01, 0.05, 0.15)
sc_data_result = cmest_multistate(data = sc_data,
s = time_to_predict_sc,
multistate_seed = 1,
exposure = 'A', mediator = 'M', outcome = 'S',
yevent = "ind_S", mevent = "ind_M",
basec = c("C1", "C2"),
basecval = c("C1" = "1",
"C2" = as.character(mean(sc_data$C2))),
astar="1", a="2",
nboot=1, EMint=F,
bh_method = "breslow")
sc_data_pt_est = sc_data_result[[2]]
print(sc_data_pt_est)
# get true values
get_theo_RD_SD = function(s=1, C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1,0.8)){
a1 = a_vec[1]
a2 = a_vec[2]
a3 = a_vec[3]
b1 = b_vec[1]
b3 = b_vec[2]
b4 = b_vec[3]
c1 = c_vec[1]
c2 = c_vec[2]
c3 = c_vec[3]
c4 = c_vec[4]
d = C2
lambda1 = lambda_vec[1]
lambda2 = lambda_vec[2]
lambda3 = lambda_vec[3]
# reference and active factor level
a_low = 1 # which(levels(sc_data$A)=="b")
a_high = 2 # which(levels(sc_data$A)=="c")
P_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_low+b3*C1+b4*d)*s)
P_g_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
P_g_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
P_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_low+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_low+c2*t+c3*C1+c4*d) * (t-s))
}
P_g_01 = integrate(P_g_01_func, lower = 0, upper = s)
P_01 = integrate(P_01_func, lower=0, upper = s)
# theoretical RD
theoretical_RD = (P_g_00 + P_g_01$value) - (P_00 + P_01$value)
SD_1 = exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
SD_2_func = function(t){
exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_high+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
SD_2 = integrate(SD_2_func, lower = 0, upper = s)
# theoretical SD
theoretical_SD = (SD_1 + SD_2$value) - (P_g_00 + P_g_01$value)
return(c(RD = theoretical_RD, SD = theoretical_SD))
}
theo_list = list()
for (i in 1:length(time_to_predict_sc)){
theo_list[[i]] = get_theo_RD_SD(s=time_to_predict_sc[i], C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1.0,0.8))
}
theo_mat = round(do.call(rbind, theo_list), 5)
rownames(theo_mat) = time_to_predict_sc
print(theo_mat)
# compare estimates with truth under tolerance criteria
## either error margin less than 0.02
## or abs((estimate-true)/true) < 20%
cond1 = abs(sc_data_pt_est[,c("RD", "SD")] - theo_mat) <= 0.025
cond2 = abs((sc_data_pt_est[,c("RD", "SD")] - theo_mat)/theo_mat) <= 0.25
combined_cond = cond1 | cond2
expect_true(all(combined_cond))
})
test_that("cmest_multistate correctly estimates RD and SD for a continuous exposure", {
# generate data
# set up coefficients
# M (trans 1)
a1 = -1.9
a2 = 0.2
a3 = 0.5
# S (trans 2)
b1 = 1
b3 = -0.5
b4 = 0.3
# M when generating the semi-competing observations in resample() (trans 3)
# S (resample) (trans 3)
c1 = 0.55
c2 = -0.15
c3 = -0.1
c4 = -0.2
# generate dataset
set.seed(8)
# build a function to generate time-to-event data
gen_srv <- function(n, lambda, beta, X){
X = as.matrix(X)
beta = as.matrix(beta, ncol=1)
time = -log(runif(n)) / (lambda * exp(X %*% beta)) # exponential distribution
return(time)
}
n <- 1000
#A = sample(c(0,1),replace=TRUE, size=n, c(0.5,0.5)) #binary exposure
A = rnorm(n, mean=0.7, sd=0.1) #continuous exposure
C1 = sample(c(0,1),replace=TRUE, size=n,c(0.6, 0.4)) #binary confounder
C2 = rnorm(n, mean = 1, sd = 1) #continuous confounder
id=c(1:n)
full = data.frame(id,A,C1,C2)
M = gen_srv(n=n, lambda = 1.5, beta = c(a1,a2,a3), X=data.frame(A,C1,C2)) #time to event mediator
S = gen_srv(n=n, lambda = 1, beta = c(b1,b3,b4), X=data.frame(A,C1,C2)) #time to event outcome
data = data.frame(id = c(1:n), M = M, S = S)
# indicator for event
data$ind_M = ifelse(data$M <= data$S, 1, 0)
data$ind_S = 1
data <- merge(data,full , by = "id")
#modify Y distribution
trans_matrix = mstate::transMat(x = list(c(2, 3), c(3), c()), names = c("A", "M", "S"))
covs = c("A","M", "C1","C2")
pre_data = mstate::msprep(time = c(NA, "M", "S"), status = c(NA, "ind_M", "ind_S"),
data = data, trans = trans_matrix, keep = covs)
pre_data = mstate::expand.covs(pre_data, covs, append = TRUE, longnames = FALSE)
#pre_data$A_M.3 = pre_data$A.3*pre_data$M.3
# resample for T < S
data_23 = pre_data[which(pre_data$trans == 3),]
data_23_tem = data.frame(id = rep(NA,dim(data_23)[1]),
new_y = rep(NA,dim(data_23)[1]))
paste("# to resample is ", nrow(data_23))
for(i in 1:dim(data_23)[1]){
data_23_tem$id[i] = data_23$id[i]
repeat {
time_test = gen_srv(n = 1,
lambda = 0.8,
beta = c(as.numeric(c1),
c2,
as.numeric(c3),
as.numeric(c4)),
X = data_23[i, c("A.3", "M.3", "C1.3","C2.3")])
# exit if the condition is met
if (time_test > data_23[i,"M.3"]) break
}
data_23_tem$new_y[i] = time_test
}
data_temp = merge(data, data_23_tem, by = "id", all = T)
# modify Y and M
data_temp$S[which(data_temp$ind_M == 1)] = data_temp$new_y[which(data_temp$ind_M == 1)]
data_temp$M[which(data_temp$ind_M == 0)] = data_temp$S[which(data_temp$ind_M == 0)]
data_final = data_temp
#data_final$A = as.factor(data_final$A) #generate a factor exposure
sc_data = data_final %>% dplyr::select(id,A,M,S,ind_M,ind_S,C1,C2)
#print(summary(sc_data$S))
# generate time to censoring C; compare C to S; update event indicator
time_to_censor = runif(n, 0, 2*max(sc_data$S))
sc_data$ind_S = ifelse(sc_data$S > time_to_censor, 0, 1)
sc_data$C1 = factor(sc_data$C1)
# get cmest_multistate estimate
time_to_predict_sc = c(0.1, 0.35, 0.7, 1)
# 2.5% and 97.5% quantile for the continuous exposure
a_low = qnorm(0.25, 0.7, 0.1, lower.tail=T)
a_high = qnorm(0.75, 0.7, 0.1, lower.tail=T)
sc_data_result = cmest_multistate(data = sc_data,
s = time_to_predict_sc,
multistate_seed = 1,
exposure = 'A', mediator = 'M', outcome = 'S',
yevent = "ind_S", mevent = "ind_M",
basec = c("C1", "C2"),
basecval = c("C1" = "1",
"C2" = as.character(mean(sc_data$C2))),
astar=as.character(a_low), a=as.character(a_high),
nboot=1, EMint=F,
bh_method = "breslow")
sc_data_pt_est = sc_data_result[[2]]
#print(sc_data_pt_est)
# get true values
get_theo_RD_SD = function(s=1, C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1,0.8)){
a1 = a_vec[1]
a2 = a_vec[2]
a3 = a_vec[3]
b1 = b_vec[1]
b3 = b_vec[2]
b4 = b_vec[3]
c1 = c_vec[1]
c2 = c_vec[2]
c3 = c_vec[3]
c4 = c_vec[4]
d = C2
lambda1 = lambda_vec[1]
lambda2 = lambda_vec[2]
lambda3 = lambda_vec[3]
# 2.5% and 97.5% quantile for the continuous exposure
a_low = qnorm(0.25, 0.7, 0.1, lower.tail=T)
a_high = qnorm(0.75, 0.7, 0.1, lower.tail=T)
P_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_low+b3*C1+b4*d)*s)
P_g_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
P_g_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
P_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_low+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_low+c2*t+c3*C1+c4*d) * (t-s))
}
P_g_01 = integrate(P_g_01_func, lower = 0, upper = s)
P_01 = integrate(P_01_func, lower=0, upper = s)
# theoretical RD
theoretical_RD = (P_g_00 + P_g_01$value) - (P_00 + P_01$value)
SD_1 = exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
SD_2_func = function(t){
exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_high+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
SD_2 = integrate(SD_2_func, lower = 0, upper = s)
# theoretical SD
theoretical_SD = (SD_1 + SD_2$value) - (P_g_00 + P_g_01$value)
return(c(RD = theoretical_RD, SD = theoretical_SD))
}
theo_list = list()
for (i in 1:length(time_to_predict_sc)){
theo_list[[i]] = get_theo_RD_SD(s=time_to_predict_sc[i], C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1.0,0.8))
}
theo_mat = round(do.call(rbind, theo_list), 5)
rownames(theo_mat) = time_to_predict_sc
print(theo_mat)
# compare estimates with truth under tolerance criteria
## either error margin less than 0.02
## or abs((estimate-true)/true) < 20%
cond1 = abs(sc_data_pt_est[,c("RD", "SD")] - theo_mat) <= 0.025
cond2 = abs((sc_data_pt_est[,c("RD", "SD")] - theo_mat)/theo_mat) <= 0.25
combined_cond = cond1 | cond2
expect_true(all(combined_cond))
})
test_that("cmest_multistate correctly estimates RD and SD for a binary exposure", {
# generate data
# set up coefficients
# M (trans 1)
a1 = -1.9
a2 = 0.2
a3 = 0.5
# S (trans 2)
b1 = 1
b3 = -0.5
b4 = 0.3
# M when generating the semi-competing observations in resample() (trans 3)
# S (resample) (trans 3)
c1 = 0.55
c2 = -0.15
c3 = -0.1
c4 = -0.2
# generate dataset
set.seed(8)
# build a function to generate time-to-event data
gen_srv <- function(n, lambda, beta, X){
X = as.matrix(X)
beta = as.matrix(beta, ncol=1)
time = -log(runif(n)) / (lambda * exp(X %*% beta)) # exponential distribution
return(time)
}
n <- 1000
A = sample(c(0,1),replace=TRUE, size=n, c(0.5,0.5)) #binary exposure
C1 = sample(c(0,1),replace=TRUE, size=n,c(0.6, 0.4)) #binary confounder
C2 = rnorm(n, mean = 1, sd = 1) #continuous confounder
id=c(1:n)
full = data.frame(id,A,C1,C2)
M = gen_srv(n=n, lambda = 1.5, beta = c(a1,a2,a3), X=data.frame(A,C1,C2)) #time to event mediator
S = gen_srv(n=n, lambda = 1, beta = c(b1,b3,b4), X=data.frame(A,C1,C2)) #time to event outcome
data = data.frame(id = c(1:n), M = M, S = S)
# indicator for event
data$ind_M = ifelse(data$M <= data$S, 1, 0)
data$ind_S = 1
data <- merge(data,full , by = "id")
#modify Y distribution
trans_matrix = mstate::transMat(x = list(c(2, 3), c(3), c()), names = c("A", "M", "S"))
covs = c("A","M", "C1","C2")
pre_data = mstate::msprep(time = c(NA, "M", "S"), status = c(NA, "ind_M", "ind_S"),
data = data, trans = trans_matrix, keep = covs)
pre_data = mstate::expand.covs(pre_data, covs, append = TRUE, longnames = FALSE)
#pre_data$A_M.3 = pre_data$A.3*pre_data$M.3
# resample for T < S
data_23 = pre_data[which(pre_data$trans == 3),]
data_23_tem = data.frame(id = rep(NA,dim(data_23)[1]),
new_y = rep(NA,dim(data_23)[1]))
paste("# to resample is ", nrow(data_23))
for(i in 1:dim(data_23)[1]){
data_23_tem$id[i] = data_23$id[i]
repeat {
time_test = gen_srv(n = 1,
lambda = 0.8,
beta = c(as.numeric(c1),
c2,
as.numeric(c3),
as.numeric(c4)),
X = data_23[i, c("A.3", "M.3", "C1.3","C2.3")])
# exit if the condition is met
if (time_test > data_23[i,"M.3"]) break
}
data_23_tem$new_y[i] = time_test
}
data_temp = merge(data, data_23_tem, by = "id", all = T)
# modify Y and M
data_temp$S[which(data_temp$ind_M == 1)] = data_temp$new_y[which(data_temp$ind_M == 1)]
data_temp$M[which(data_temp$ind_M == 0)] = data_temp$S[which(data_temp$ind_M == 0)]
data_final = data_temp
data_final$A = as.factor(data_final$A) #generate a factor exposure
sc_data = data_final %>% dplyr::select(id,A,M,S,ind_M,ind_S,C1,C2)
# generate time to censoring C; compare C to S; update event indicator
time_to_censor = runif(n, 0, 2*max(sc_data$S))
sc_data$ind_S = ifelse(sc_data$S > time_to_censor, 0, 1)
sc_data$A = factor(sc_data$A)
sc_data$C1 = factor(sc_data$C1)
# get cmest_multistate estimate
time_to_predict_sc = c(0.1, 0.5, 1, 2, 3, 4, 5, 6)
sc_data_result = cmest_multistate(data = sc_data,
s = time_to_predict_sc,
multistate_seed = 1,
exposure = 'A', mediator = 'M', outcome = 'S',
yevent = "ind_S", mevent = "ind_M",
basec = c("C1", "C2"),
basecval = c("C1" = "1",
"C2" = as.character(mean(sc_data$C2))),
astar="0", a="1",
nboot=1, EMint=F,
bh_method = "breslow")
sc_data_pt_est = sc_data_result[[2]]
# get true values
get_theo_RD_SD = function(s=1, C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1,0.8)){
a1 = a_vec[1]
a2 = a_vec[2]
a3 = a_vec[3]
b1 = b_vec[1]
b3 = b_vec[2]
b4 = b_vec[3]
c1 = c_vec[1]
c2 = c_vec[2]
c3 = c_vec[3]
c4 = c_vec[4]
d = C2
lambda1 = lambda_vec[1]
lambda2 = lambda_vec[2]
lambda3 = lambda_vec[3]
P_00 = exp(-lambda1*exp(a2*C1+a3*d)*s - lambda2*exp(b3*C1+b4*d)*s)
P_g_00 = exp(-lambda1*exp(a2*C1+a3*d)*s - lambda2*exp(b1+b3*C1+b4*d)*s)
P_g_01_func = function(t){
exp(-lambda1*exp(a2*C1+a3*d)*t - lambda2*exp(b1+b3*C1+b4*d)*t) * lambda1*exp(a2*C1+a3*d) * exp(lambda3*exp(c1+c2*t+c3*C1+c4*d) * (t-s))
}
P_01_func = function(t){
exp(-lambda1*exp(a2*C1+a3*d)*t - lambda2*exp(b3*C1+b4*d)*t) * lambda1*exp(a2*C1+a3*d) * exp(lambda3*exp(c2*t+c3*C1+c4*d) * (t-s))
}
P_g_01 = integrate(P_g_01_func, lower = 0, upper = s)
P_01 = integrate(P_01_func, lower=0, upper = s)
# theoretical RD
theoretical_RD = (P_g_00 + P_g_01$value) - (P_00 + P_01$value)
SD_1 = exp(-lambda1*exp(a1+a2*C1+a3*d)*s - lambda2*exp(b1+b3*C1+b4*d)*s)
SD_2_func = function(t){
exp(-lambda1*exp(a1+a2*C1+a3*d)*t - lambda2*exp(b1+b3*C1+b4*d)*t) * lambda1*exp(a1+a2*C1+a3*d) * exp(lambda3*exp(c1+c2*t+c3*C1+c4*d) * (t-s))
}
SD_2 = integrate(SD_2_func, lower = 0, upper = s)
# theoretical SD
theoretical_SD = (SD_1 + SD_2$value) - (P_g_00 + P_g_01$value)
return(c(RD = theoretical_RD, SD = theoretical_SD))
}
theo_list = list()
for (i in 1:length(time_to_predict_sc)){
theo_list[[i]] = get_theo_RD_SD(s=time_to_predict_sc[i], C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1.0,0.8))
}
theo_mat = round(do.call(rbind, theo_list), 5)
rownames(theo_mat) = time_to_predict_sc
# compare estimates with truth under tolerance criteria
## either error margin less than 0.02
## or abs((estimate-true)/true) < 20%
cond1 = abs(sc_data_pt_est[,c("RD", "SD")] - theo_mat) <= 0.025
cond2 = abs((sc_data_pt_est[,c("RD", "SD")] - theo_mat)/theo_mat) <= 0.25
combined_cond = cond1 | cond2
expect_true(all(combined_cond))
})
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context("cmest_multistate works correctly")
test_that("cmest_multistate correctly estimates RD and SD for a multicategorical exposure", {
# generate data
# set up coefficients
# M (trans 1)
a1 = -1.9
a2 = 0.2
a3 = 0.5
# S (trans 2)
b1 = 1
b3 = -0.5
b4 = 0.3
# M when generating the semi-competing observations in resample() (trans 3)
# S (resample) (trans 3)
c1 = 0.55
c2 = -0.15
c3 = -0.1
c4 = -0.2
# generate dataset
set.seed(8)
# build a function to generate time-to-event data
gen_srv <- function(n, lambda, beta, X){
X = as.matrix(X)
beta = as.matrix(beta, ncol=1)
time = -log(runif(n)) / (lambda * exp(X %*% beta)) # exponential distribution
return(time)
}
n <- 1000
A = sample(c(1,2,3),replace=TRUE, size=n, c(0.2,0.3,0.5)) #multi-categorical exposure
C1 = sample(c(0,1),replace=TRUE, size=n,c(0.6, 0.4)) #binary confounder
C2 = rnorm(n, mean = 1, sd = 1) #continuous confounder
id=c(1:n)
full = data.frame(id,A,C1,C2)
M = gen_srv(n=n, lambda = 1.5, beta = c(a1,a2,a3), X=data.frame(A,C1,C2)) #time to event mediator
S = gen_srv(n=n, lambda = 1, beta = c(b1,b3,b4), X=data.frame(A,C1,C2)) #time to event outcome
data = data.frame(id = c(1:n), M = M, S = S)
# indicator for event
data$ind_M = ifelse(data$M <= data$S, 1, 0)
data$ind_S = 1
data <- merge(data,full , by = "id")
#modify Y distribution
trans_matrix = mstate::transMat(x = list(c(2, 3), c(3), c()), names = c("A", "M", "S"))
covs = c("A","M", "C1","C2")
pre_data = mstate::msprep(time = c(NA, "M", "S"), status = c(NA, "ind_M", "ind_S"),
data = data, trans = trans_matrix, keep = covs)
pre_data = mstate::expand.covs(pre_data, covs, append = TRUE, longnames = FALSE)
#pre_data$A_M.3 = pre_data$A.3*pre_data$M.3
# resample for T < S
data_23 = pre_data[which(pre_data$trans == 3),]
data_23_tem = data.frame(id = rep(NA,dim(data_23)[1]),
new_y = rep(NA,dim(data_23)[1]))
paste("# to resample is ", nrow(data_23))
for(i in 1:dim(data_23)[1]){
data_23_tem$id[i] = data_23$id[i]
repeat {
time_test = gen_srv(n = 1,
lambda = 0.8,
beta = c(as.numeric(c1),
c2,
as.numeric(c3),
as.numeric(c4)),
X = data_23[i, c("A.3", "M.3", "C1.3","C2.3")])
# exit if the condition is met
if (time_test > data_23[i,"M.3"]) break
}
data_23_tem$new_y[i] = time_test
}
data_temp = merge(data, data_23_tem, by = "id", all = T)
# modify Y and M
data_temp$S[which(data_temp$ind_M == 1)] = data_temp$new_y[which(data_temp$ind_M == 1)]
data_temp$M[which(data_temp$ind_M == 0)] = data_temp$S[which(data_temp$ind_M == 0)]
data_final = data_temp
data_final$A = as.factor(data_final$A) #generate a factor exposure
sc_data = data_final %>% dplyr::select(id,A,M,S,ind_M,ind_S,C1,C2)
# generate time to censoring C; compare C to S; update event indicator
time_to_censor = runif(n, 0, 2*max(sc_data$S))
sc_data$ind_S = ifelse(sc_data$S > time_to_censor, 0, 1)
sc_data$A = factor(sc_data$A)
sc_data$C1 = factor(sc_data$C1)
print(summary(sc_data$S))
# get cmest_multistate estimate
time_to_predict_sc = c(0.01, 0.05, 0.15)
sc_data_result = cmest_multistate(data = sc_data,
s = time_to_predict_sc,
multistate_seed = 1,
exposure = 'A', mediator = 'M', outcome = 'S',
yevent = "ind_S", mevent = "ind_M",
basec = c("C1", "C2"),
basecval = c("C1" = "1",
"C2" = as.character(mean(sc_data$C2))),
astar="1", a="2",
nboot=1, EMint=F,
bh_method = "breslow")
sc_data_pt_est = sc_data_result[[2]]
print(sc_data_pt_est)
# get true values
get_theo_RD_SD = function(s=1, C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1,0.8)){
a1 = a_vec[1]
a2 = a_vec[2]
a3 = a_vec[3]
b1 = b_vec[1]
b3 = b_vec[2]
b4 = b_vec[3]
c1 = c_vec[1]
c2 = c_vec[2]
c3 = c_vec[3]
c4 = c_vec[4]
d = C2
lambda1 = lambda_vec[1]
lambda2 = lambda_vec[2]
lambda3 = lambda_vec[3]
# reference and active factor level
a_low = 1 # which(levels(sc_data$A)=="b")
a_high = 2 # which(levels(sc_data$A)=="c")
P_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_low+b3*C1+b4*d)*s)
P_g_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
P_g_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
P_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_low+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_low+c2*t+c3*C1+c4*d) * (t-s))
}
P_g_01 = integrate(P_g_01_func, lower = 0, upper = s)
P_01 = integrate(P_01_func, lower=0, upper = s)
# theoretical RD
theoretical_RD = (P_g_00 + P_g_01$value) - (P_00 + P_01$value)
SD_1 = exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
SD_2_func = function(t){
exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_high+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
SD_2 = integrate(SD_2_func, lower = 0, upper = s)
# theoretical SD
theoretical_SD = (SD_1 + SD_2$value) - (P_g_00 + P_g_01$value)
return(c(RD = theoretical_RD, SD = theoretical_SD))
}
theo_list = list()
for (i in 1:length(time_to_predict_sc)){
theo_list[[i]] = get_theo_RD_SD(s=time_to_predict_sc[i], C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1.0,0.8))
}
theo_mat = round(do.call(rbind, theo_list), 5)
rownames(theo_mat) = time_to_predict_sc
print(theo_mat)
# compare estimates with truth under tolerance criteria
## either error margin less than 0.02
## or abs((estimate-true)/true) < 20%
cond1 = abs(sc_data_pt_est[,c("RD", "SD")] - theo_mat) <= 0.025
cond2 = abs((sc_data_pt_est[,c("RD", "SD")] - theo_mat)/theo_mat) <= 0.25
combined_cond = cond1 | cond2
expect_true(all(combined_cond))
})
test_that("cmest_multistate correctly estimates RD and SD for a continuous exposure", {
# generate data
# set up coefficients
# M (trans 1)
a1 = -1.9
a2 = 0.2
a3 = 0.5
# S (trans 2)
b1 = 1
b3 = -0.5
b4 = 0.3
# M when generating the semi-competing observations in resample() (trans 3)
# S (resample) (trans 3)
c1 = 0.55
c2 = -0.15
c3 = -0.1
c4 = -0.2
# generate dataset
set.seed(8)
# build a function to generate time-to-event data
gen_srv <- function(n, lambda, beta, X){
X = as.matrix(X)
beta = as.matrix(beta, ncol=1)
time = -log(runif(n)) / (lambda * exp(X %*% beta)) # exponential distribution
return(time)
}
n <- 1000
#A = sample(c(0,1),replace=TRUE, size=n, c(0.5,0.5)) #binary exposure
A = rnorm(n, mean=0.7, sd=0.1) #continuous exposure
C1 = sample(c(0,1),replace=TRUE, size=n,c(0.6, 0.4)) #binary confounder
C2 = rnorm(n, mean = 1, sd = 1) #continuous confounder
id=c(1:n)
full = data.frame(id,A,C1,C2)
M = gen_srv(n=n, lambda = 1.5, beta = c(a1,a2,a3), X=data.frame(A,C1,C2)) #time to event mediator
S = gen_srv(n=n, lambda = 1, beta = c(b1,b3,b4), X=data.frame(A,C1,C2)) #time to event outcome
data = data.frame(id = c(1:n), M = M, S = S)
# indicator for event
data$ind_M = ifelse(data$M <= data$S, 1, 0)
data$ind_S = 1
data <- merge(data,full , by = "id")
#modify Y distribution
trans_matrix = mstate::transMat(x = list(c(2, 3), c(3), c()), names = c("A", "M", "S"))
covs = c("A","M", "C1","C2")
pre_data = mstate::msprep(time = c(NA, "M", "S"), status = c(NA, "ind_M", "ind_S"),
data = data, trans = trans_matrix, keep = covs)
pre_data = mstate::expand.covs(pre_data, covs, append = TRUE, longnames = FALSE)
#pre_data$A_M.3 = pre_data$A.3*pre_data$M.3
# resample for T < S
data_23 = pre_data[which(pre_data$trans == 3),]
data_23_tem = data.frame(id = rep(NA,dim(data_23)[1]),
new_y = rep(NA,dim(data_23)[1]))
paste("# to resample is ", nrow(data_23))
for(i in 1:dim(data_23)[1]){
data_23_tem$id[i] = data_23$id[i]
repeat {
time_test = gen_srv(n = 1,
lambda = 0.8,
beta = c(as.numeric(c1),
c2,
as.numeric(c3),
as.numeric(c4)),
X = data_23[i, c("A.3", "M.3", "C1.3","C2.3")])
# exit if the condition is met
if (time_test > data_23[i,"M.3"]) break
}
data_23_tem$new_y[i] = time_test
}
data_temp = merge(data, data_23_tem, by = "id", all = T)
# modify Y and M
data_temp$S[which(data_temp$ind_M == 1)] = data_temp$new_y[which(data_temp$ind_M == 1)]
data_temp$M[which(data_temp$ind_M == 0)] = data_temp$S[which(data_temp$ind_M == 0)]
data_final = data_temp
#data_final$A = as.factor(data_final$A) #generate a factor exposure
sc_data = data_final %>% dplyr::select(id,A,M,S,ind_M,ind_S,C1,C2)
#print(summary(sc_data$S))
# generate time to censoring C; compare C to S; update event indicator
time_to_censor = runif(n, 0, 2*max(sc_data$S))
sc_data$ind_S = ifelse(sc_data$S > time_to_censor, 0, 1)
sc_data$C1 = factor(sc_data$C1)
# get cmest_multistate estimate
time_to_predict_sc = c(0.1, 0.35, 0.7, 1)
# 2.5% and 97.5% quantile for the continuous exposure
a_low = qnorm(0.25, 0.7, 0.1, lower.tail=T)
a_high = qnorm(0.75, 0.7, 0.1, lower.tail=T)
sc_data_result = cmest_multistate(data = sc_data,
s = time_to_predict_sc,
multistate_seed = 1,
exposure = 'A', mediator = 'M', outcome = 'S',
yevent = "ind_S", mevent = "ind_M",
basec = c("C1", "C2"),
basecval = c("C1" = "1",
"C2" = as.character(mean(sc_data$C2))),
astar=as.character(a_low), a=as.character(a_high),
nboot=1, EMint=F,
bh_method = "breslow")
sc_data_pt_est = sc_data_result[[2]]
#print(sc_data_pt_est)
# get true values
get_theo_RD_SD = function(s=1, C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1,0.8)){
a1 = a_vec[1]
a2 = a_vec[2]
a3 = a_vec[3]
b1 = b_vec[1]
b3 = b_vec[2]
b4 = b_vec[3]
c1 = c_vec[1]
c2 = c_vec[2]
c3 = c_vec[3]
c4 = c_vec[4]
d = C2
lambda1 = lambda_vec[1]
lambda2 = lambda_vec[2]
lambda3 = lambda_vec[3]
# 2.5% and 97.5% quantile for the continuous exposure
a_low = qnorm(0.25, 0.7, 0.1, lower.tail=T)
a_high = qnorm(0.75, 0.7, 0.1, lower.tail=T)
P_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_low+b3*C1+b4*d)*s)
P_g_00 = exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
P_g_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
P_01_func = function(t){
exp(-lambda1*exp(a1*a_low+a2*C1+a3*d)*t - lambda2*exp(b1*a_low+b3*C1+b4*d)*t) * lambda1*exp(a1*a_low+a2*C1+a3*d) * exp(lambda3*exp(c1*a_low+c2*t+c3*C1+c4*d) * (t-s))
}
P_g_01 = integrate(P_g_01_func, lower = 0, upper = s)
P_01 = integrate(P_01_func, lower=0, upper = s)
# theoretical RD
theoretical_RD = (P_g_00 + P_g_01$value) - (P_00 + P_01$value)
SD_1 = exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*s - lambda2*exp(b1*a_high+b3*C1+b4*d)*s)
SD_2_func = function(t){
exp(-lambda1*exp(a1*a_high+a2*C1+a3*d)*t - lambda2*exp(b1*a_high+b3*C1+b4*d)*t) * lambda1*exp(a1*a_high+a2*C1+a3*d) * exp(lambda3*exp(c1*a_high+c2*t+c3*C1+c4*d) * (t-s))
}
SD_2 = integrate(SD_2_func, lower = 0, upper = s)
# theoretical SD
theoretical_SD = (SD_1 + SD_2$value) - (P_g_00 + P_g_01$value)
return(c(RD = theoretical_RD, SD = theoretical_SD))
}
theo_list = list()
for (i in 1:length(time_to_predict_sc)){
theo_list[[i]] = get_theo_RD_SD(s=time_to_predict_sc[i], C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1.0,0.8))
}
theo_mat = round(do.call(rbind, theo_list), 5)
rownames(theo_mat) = time_to_predict_sc
print(theo_mat)
# compare estimates with truth under tolerance criteria
## either error margin less than 0.02
## or abs((estimate-true)/true) < 20%
cond1 = abs(sc_data_pt_est[,c("RD", "SD")] - theo_mat) <= 0.025
cond2 = abs((sc_data_pt_est[,c("RD", "SD")] - theo_mat)/theo_mat) <= 0.25
combined_cond = cond1 | cond2
expect_true(all(combined_cond))
})
test_that("cmest_multistate correctly estimates RD and SD for a binary exposure", {
# generate data
# set up coefficients
# M (trans 1)
a1 = -1.9
a2 = 0.2
a3 = 0.5
# S (trans 2)
b1 = 1
b3 = -0.5
b4 = 0.3
# M when generating the semi-competing observations in resample() (trans 3)
# S (resample) (trans 3)
c1 = 0.55
c2 = -0.15
c3 = -0.1
c4 = -0.2
# generate dataset
set.seed(8)
# build a function to generate time-to-event data
gen_srv <- function(n, lambda, beta, X){
X = as.matrix(X)
beta = as.matrix(beta, ncol=1)
time = -log(runif(n)) / (lambda * exp(X %*% beta)) # exponential distribution
return(time)
}
n <- 1000
A = sample(c(0,1),replace=TRUE, size=n, c(0.5,0.5)) #binary exposure
C1 = sample(c(0,1),replace=TRUE, size=n,c(0.6, 0.4)) #binary confounder
C2 = rnorm(n, mean = 1, sd = 1) #continuous confounder
id=c(1:n)
full = data.frame(id,A,C1,C2)
M = gen_srv(n=n, lambda = 1.5, beta = c(a1,a2,a3), X=data.frame(A,C1,C2)) #time to event mediator
S = gen_srv(n=n, lambda = 1, beta = c(b1,b3,b4), X=data.frame(A,C1,C2)) #time to event outcome
data = data.frame(id = c(1:n), M = M, S = S)
# indicator for event
data$ind_M = ifelse(data$M <= data$S, 1, 0)
data$ind_S = 1
data <- merge(data,full , by = "id")
#modify Y distribution
trans_matrix = mstate::transMat(x = list(c(2, 3), c(3), c()), names = c("A", "M", "S"))
covs = c("A","M", "C1","C2")
pre_data = mstate::msprep(time = c(NA, "M", "S"), status = c(NA, "ind_M", "ind_S"),
data = data, trans = trans_matrix, keep = covs)
pre_data = mstate::expand.covs(pre_data, covs, append = TRUE, longnames = FALSE)
#pre_data$A_M.3 = pre_data$A.3*pre_data$M.3
# resample for T < S
data_23 = pre_data[which(pre_data$trans == 3),]
data_23_tem = data.frame(id = rep(NA,dim(data_23)[1]),
new_y = rep(NA,dim(data_23)[1]))
paste("# to resample is ", nrow(data_23))
for(i in 1:dim(data_23)[1]){
data_23_tem$id[i] = data_23$id[i]
repeat {
time_test = gen_srv(n = 1,
lambda = 0.8,
beta = c(as.numeric(c1),
c2,
as.numeric(c3),
as.numeric(c4)),
X = data_23[i, c("A.3", "M.3", "C1.3","C2.3")])
# exit if the condition is met
if (time_test > data_23[i,"M.3"]) break
}
data_23_tem$new_y[i] = time_test
}
data_temp = merge(data, data_23_tem, by = "id", all = T)
# modify Y and M
data_temp$S[which(data_temp$ind_M == 1)] = data_temp$new_y[which(data_temp$ind_M == 1)]
data_temp$M[which(data_temp$ind_M == 0)] = data_temp$S[which(data_temp$ind_M == 0)]
data_final = data_temp
data_final$A = as.factor(data_final$A) #generate a factor exposure
sc_data = data_final %>% dplyr::select(id,A,M,S,ind_M,ind_S,C1,C2)
# generate time to censoring C; compare C to S; update event indicator
time_to_censor = runif(n, 0, 2*max(sc_data$S))
sc_data$ind_S = ifelse(sc_data$S > time_to_censor, 0, 1)
sc_data$A = factor(sc_data$A)
sc_data$C1 = factor(sc_data$C1)
# get cmest_multistate estimate
time_to_predict_sc = c(0.1, 0.5, 1, 2, 3, 4, 5, 6)
sc_data_result = cmest_multistate(data = sc_data,
s = time_to_predict_sc,
multistate_seed = 1,
exposure = 'A', mediator = 'M', outcome = 'S',
yevent = "ind_S", mevent = "ind_M",
basec = c("C1", "C2"),
basecval = c("C1" = "1",
"C2" = as.character(mean(sc_data$C2))),
astar="0", a="1",
nboot=1, EMint=F,
bh_method = "breslow")
sc_data_pt_est = sc_data_result[[2]]
# get true values
get_theo_RD_SD = function(s=1, C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1,0.8)){
a1 = a_vec[1]
a2 = a_vec[2]
a3 = a_vec[3]
b1 = b_vec[1]
b3 = b_vec[2]
b4 = b_vec[3]
c1 = c_vec[1]
c2 = c_vec[2]
c3 = c_vec[3]
c4 = c_vec[4]
d = C2
lambda1 = lambda_vec[1]
lambda2 = lambda_vec[2]
lambda3 = lambda_vec[3]
P_00 = exp(-lambda1*exp(a2*C1+a3*d)*s - lambda2*exp(b3*C1+b4*d)*s)
P_g_00 = exp(-lambda1*exp(a2*C1+a3*d)*s - lambda2*exp(b1+b3*C1+b4*d)*s)
P_g_01_func = function(t){
exp(-lambda1*exp(a2*C1+a3*d)*t - lambda2*exp(b1+b3*C1+b4*d)*t) * lambda1*exp(a2*C1+a3*d) * exp(lambda3*exp(c1+c2*t+c3*C1+c4*d) * (t-s))
}
P_01_func = function(t){
exp(-lambda1*exp(a2*C1+a3*d)*t - lambda2*exp(b3*C1+b4*d)*t) * lambda1*exp(a2*C1+a3*d) * exp(lambda3*exp(c2*t+c3*C1+c4*d) * (t-s))
}
P_g_01 = integrate(P_g_01_func, lower = 0, upper = s)
P_01 = integrate(P_01_func, lower=0, upper = s)
# theoretical RD
theoretical_RD = (P_g_00 + P_g_01$value) - (P_00 + P_01$value)
SD_1 = exp(-lambda1*exp(a1+a2*C1+a3*d)*s - lambda2*exp(b1+b3*C1+b4*d)*s)
SD_2_func = function(t){
exp(-lambda1*exp(a1+a2*C1+a3*d)*t - lambda2*exp(b1+b3*C1+b4*d)*t) * lambda1*exp(a1+a2*C1+a3*d) * exp(lambda3*exp(c1+c2*t+c3*C1+c4*d) * (t-s))
}
SD_2 = integrate(SD_2_func, lower = 0, upper = s)
# theoretical SD
theoretical_SD = (SD_1 + SD_2$value) - (P_g_00 + P_g_01$value)
return(c(RD = theoretical_RD, SD = theoretical_SD))
}
theo_list = list()
for (i in 1:length(time_to_predict_sc)){
theo_list[[i]] = get_theo_RD_SD(s=time_to_predict_sc[i], C1=1, C2=1,
a_vec=c(a1, a2, a3),
b_vec=c(b1, b3, b4),
c_vec=c(c1, c2, c3, c4),
lambda_vec=c(1.5,1.0,0.8))
}
theo_mat = round(do.call(rbind, theo_list), 5)
rownames(theo_mat) = time_to_predict_sc
# compare estimates with truth under tolerance criteria
## either error margin less than 0.02
## or abs((estimate-true)/true) < 20%
cond1 = abs(sc_data_pt_est[,c("RD", "SD")] - theo_mat) <= 0.025
cond2 = abs((sc_data_pt_est[,c("RD", "SD")] - theo_mat)/theo_mat) <= 0.25
combined_cond = cond1 | cond2
expect_true(all(combined_cond))
})
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