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Figure2&S1.R
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Figure2&S1.R
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# Code to generate Figure 2
library("ggplot2")
library("grid")
library("gridExtra")
set.seed(3)
t_lag <- 1
n = 1
T = 200
model = "SL"
time <- rep(1:T, n)
id <- rep(1:n, rep(T, n))
x.trt <- data.frame(matrix(rnorm(n * T * 5), nrow = n * T))
a_lag1 = rep(0, n * T)
a = NULL
ps = NULL
for (i in 1:(n * T)) {
a[i] <- rbinom(1, 1, expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5)))
ps[i] <- expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5))
a_lag1[i+1] <- a[i]
}
a_lag1 = a_lag1[1:(n * T)]
a_lag1[seq(1,n*T,T)] <- 0
y <- rnorm(mean= (a * 3 + a_lag1 * 1 + rowMeans(x.trt)), n * T)
dat <- data.frame(time = time ,id = id, y = y, a = a)
delta.seq <- exp(seq(-2.3, 2.3, length.out = 50))
test <- ts_ipsi.ipw(dat = dat, x.trt, t_lag = t_lag, delta.seq = delta.seq, nsplits = 1, model = model)
true <- ts_ipsi_true.ipw(dat = dat, x.trt, t_lag = t_lag, delta.seq = delta.seq, nsplits = 1)
Sim100 = data.frame(logOR = log(delta.seq),
estimated = colMeans(test$est.eff),
true = colMeans(true$est.eff),
sd_true = 1/(T-t_lag) * sqrt(colSums(test$est.var1.ipw)))
n <- 1
T <- 1000
model = "SL"
time <- rep(1:T, n)
id <- rep(1:n, rep(T, n))
x.trt <- data.frame(matrix(rnorm(n * T * 5), nrow = n * T))
a_lag1 = rep(0, n * T)
a = NULL
for (i in 1:(n * T)) {
a[i] <- rbinom(1, 1, expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5)))
a_lag1[i+1] <- a[i]
}
a_lag1 = a_lag1[1:(n * T)]
a_lag1[seq(1,n*T,T)] <- 0
y <- rnorm(mean= (a * 3 + a_lag1 * 1 + rowMeans(x.trt)), n * T)
dat <- data.frame(time = time ,id = id, y = y, a = a)
delta.seq <- exp(seq(-2.3, 2.3, length.out = 50))
test <- ts_ipsi.ipw(dat = dat, x.trt, t_lag = t_lag, delta.seq = delta.seq, nsplits = 1, model = model)
true <- ts_ipsi_true.ipw(dat = dat, x.trt, t_lag = t_lag, delta.seq = delta.seq, nsplits = 1)
Sim1000 = data.frame(logOR = log(delta.seq),
estimated = colMeans(test$est.eff),
true = colMeans(true$est.eff),
sd_true = 1/(T-t_lag) * sqrt(colSums(test$est.var1.ipw)))
n <- 1
T <- 5000
model = "SL"
time <- rep(1:T, n)
id <- rep(1:n, rep(T, n))
x.trt <- data.frame(matrix(rnorm(n * T * 5), nrow = n * T))
a_lag1 = rep(0, n * T)
a = NULL
for (i in 1:(n * T)) {
a[i] <- rbinom(1, 1, expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5)))
a_lag1[i+1] <- a[i]
}
a_lag1 = a_lag1[1:(n * T)]
a_lag1[seq(1,n*T,T)] <- 0
y <- rnorm(mean= (a * 3 + a_lag1 * 1 + rowMeans(x.trt)), n * T)
dat <- data.frame(time = time ,id = id, y = y, a = a)
delta.seq <- exp(seq(-2.3, 2.3, length.out = 50))
test <- ts_ipsi.ipw(dat = dat, x.trt, t_lag = t_lag, delta.seq = delta.seq, nsplits = 1, model = model)
true <- ts_ipsi_true.ipw(dat = dat, x.trt, t_lag = t_lag, delta.seq = delta.seq, nsplits = 1)
Sim5000 = data.frame(logOR = log(delta.seq),
estimated = colMeans(test$est.eff),
true = colMeans(true$est.eff),
sd_true = 1/(T-t_lag) * sqrt(colSums(test$est.var1.ipw)))
p100 = ggplot(data=Sim100, aes(x = logOR, y = estimated)) +
geom_line(aes(x= logOR, y= estimated) ,color="red", lwd=1.2) +
geom_line(aes(x= logOR, y= true) ,color="blue", lwd=1.2) +
geom_line(aes(x= logOR, y= estimated+1.96*sd_true),linetype="dashed" ,color="red", lwd=1.2) +
geom_line(aes(x= logOR, y= estimated-1.96*sd_true),linetype="dashed" ,color="red" , lwd=1.2) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size = 24),
legend.position = c(0.7, 0.2),
text = element_text(size=20),
axis.title.x = element_text(size = 22)) +
ggtitle(expression(paste("T = 200"))) +
xlab(expression(paste("Log ", delta))) +
ylab("") +
ylim(1.45, 2.35)
p1000 = ggplot(data=Sim1000, aes(x = logOR, y = estimated)) +
geom_line(aes(x= logOR, y= estimated) ,color="red", lwd=1.2) +
geom_line(aes(x= logOR, y= true) ,color="blue", lwd=1.2) +
geom_line(aes(x= logOR, y= estimated+1.96*sd_true),linetype="dashed" ,color="red", lwd=1.2) +
geom_line(aes(x= logOR, y= estimated-1.96*sd_true),linetype="dashed" ,color="red" , lwd=1.2) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size = 24),
legend.position = c(0.7, 0.2),
text = element_text(size=20),
axis.title.x = element_text(size = 22)) +
ggtitle(expression(paste("T = 1000"))) +
xlab(expression(paste("Log ", delta))) +
ylab("") +
ylim(1.45, 2.35)
p5000 = ggplot(data=Sim5000, aes(x = logOR, y = estimated)) +
geom_line(aes(x= logOR, y= estimated) ,color="red", lwd=1.2) +
geom_line(aes(x= logOR, y= true) ,color="blue", lwd=1.2) +
geom_line(aes(x= logOR, y= estimated+1.96*sd_true),linetype="dashed" ,color="red", lwd=1.2) +
geom_line(aes(x= logOR, y= estimated-1.96*sd_true),linetype="dashed" ,color="red" , lwd=1.2) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size = 24),
legend.position = c(0.7, 0.2),
text = element_text(size=20),
axis.title.x = element_text(size = 22)) +
ggtitle(expression(paste("T = 5000"))) +
xlab(expression(paste("Log ", delta))) +
ylab("") +
ylim(1.45, 2.35)
grid.arrange(p100, p1000, p5000,
nrow = 1)
save(Sim100, Sim1000, Sim5000, file = paste0("Sim_", t_lag, ".RData"))
# Code to generate Figure S1
set.seed(3)
n = 1
T = 200
model = "SL"
time <- rep(1:T, n)
id <- rep(1:n, rep(T, n))
x.trt <- data.frame(matrix(rnorm(n * T * 5), nrow = n * T))
a_lag1 = rep(0, n * T)
a = NULL
ps = NULL
for (i in 1:(n * T)) {
a[i] <- rbinom(1, 1, expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5)))
ps[i] <- expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5))
a_lag1[i+1] <- a[i]
}
ps_a = data.frame(cbind(a, ps))
colnames(ps_a) <- c("Treatment", "ps")
ps_a$Treatment[ps_a$Treatment == 1] <- "treated"
ps_a$Treatment[ps_a$Treatment == 0] <- "untreated"
q1 <- ggplot(ps_a, aes(x = ps, fill = Treatment)) +
geom_density() +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size = 24),
legend.position = c(0.7, 0.8),
text = element_text(size=20),
axis.title.x = element_text(size = 20)) +
ggtitle(expression(paste("T = 200"))) +
xlab("time-varying propensity scores")
n = 1
T = 1000
model = "SL"
time <- rep(1:T, n)
id <- rep(1:n, rep(T, n))
x.trt <- data.frame(matrix(rnorm(n * T * 5), nrow = n * T))
a_lag1 = rep(0, n * T)
a = NULL
ps = NULL
for (i in 1:(n * T)) {
a[i] <- rbinom(1, 1, expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5)))
ps[i] <- expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5))
a_lag1[i+1] <- a[i]
}
ps_a = data.frame(cbind(a, ps))
colnames(ps_a) <- c("Treatment", "ps")
ps_a$Treatment[ps_a$Treatment == 1] <- "treated"
ps_a$Treatment[ps_a$Treatment == 0] <- "untreated"
q2 <- ggplot(ps_a, aes(x = ps, fill = Treatment)) +
geom_density() +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size = 24),
legend.position = c(0.7, 0.8),
text = element_text(size=20),
axis.title.x = element_text(size = 20)) +
ggtitle(expression(paste("T = 1000"))) +
xlab("time-varying propensity scores")
n = 1
T = 5000
model = "SL"
time <- rep(1:T, n)
id <- rep(1:n, rep(T, n))
x.trt <- data.frame(matrix(rnorm(n * T * 5), nrow = n * T))
a_lag1 = rep(0, n * T)
a = NULL
ps = NULL
for (i in 1:(n * T)) {
a[i] <- rbinom(1, 1, expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5)))
ps[i] <- expit(10*(rowMeans(x.trt)[i] - a_lag1[i] + 0.5))
a_lag1[i+1] <- a[i]
}
ps_a = data.frame(cbind(a, ps))
colnames(ps_a) <- c("Treatment", "ps")
ps_a$Treatment[ps_a$Treatment == 1] <- "treated"
ps_a$Treatment[ps_a$Treatment == 0] <- "untreated"
q3 <- ggplot(ps_a, aes(x = ps, fill = Treatment)) +
geom_density() +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size = 24),
legend.position = c(0.7, 0.8),
text = element_text(size=20),
axis.title.x = element_text(size = 20)) +
ggtitle(expression(paste("T = 5000"))) +
xlab("time-varying propensity scores")
grid.arrange(q1, q2, q3,
nrow = 1,
top = textGrob(expression(paste()), gp = gpar(fontsize=28)))