surv_eff_mod: Calculate effect modification from survtmle fit objects

In hoffmakl/tmleplus: What the Package Does (One Line, Title Case)

Description

This calculations an effect modifier's difference and corresponding standard error for the marginal cumulative incidence using objects obtained from the 'survtmle' package.

Usage

surv_eff_mod(tmle_fit_1, tmle_fit_0)

Arguments

tmle_fit_1	survtmle fit object for only patients with the effect modifier
tmle_fit_0	survtmle fit object for only patients without the effect modifier
dat_full	The full data set containing all observations used to obtain each 'tmle_fit_1' and 'tmle_fit_0'.
mod_var	String containing column name of the effect modifying variable in 'dat_full'.

Examples

# Modified version of the survtmle vignette example
set.seed(1234)
n <- 1000
t_0 <- 6
trt <- rbinom(n, 1, 0.5)
eff <- rbinom(n, 1, 0.5)
adjustVars <- data.frame(W1 = round(runif(n)), W2 = round(runif(n, 0, 2)))
ftime <- round(1 + runif(n, 1, 4) - trt*eff + adjustVars$W1 + adjustVars$W2)
ftype <- round(runif(n, 0, 1))
dat <- data.frame(trt, eff, adjustVars, ftime, ftype)

# Run survtmle for all subjects without the effect modifier
dat_noeff <- dat[dat$eff == 0,]
trt <- dat_noeff$trt
adjustVars <- dat_noeff[c("W1", "W2")]
ftime <- dat_noeff$ftime
ftype <- dat_noeff$ftype
fit_noeff <- survtmle::survtmle(ftime = ftime, ftype = ftype,
                  trt = trt, adjustVars = adjustVars,
                  SL.trt = c("SL.glm", "SL.mean", "SL.step"),
                  SL.ftime = c("SL.glm", "SL.mean", "SL.step"),
                  SL.ctime = c("SL.glm", "SL.mean", "SL.step"),
                  method = "hazard", t0 = t_0)

# Run survtmle for all subjects with the effect modifier
dat_eff <- dat[dat$eff == 1,]
trt <- dat_eff$trt
adjustVars <- dat_eff[c("W1", "W2")]
ftime <- dat_eff$ftime
ftype <- dat_eff$ftype
fit_eff <- survtmle::survtmle(ftime = ftime, ftype = ftype,
                  trt = trt, adjustVars = adjustVars,
                  SL.trt = c("SL.glm", "SL.mean", "SL.step"),
                  SL.ftime = c("SL.glm", "SL.mean", "SL.step"),
                  SL.ctime = c("SL.glm", "SL.mean", "SL.step"),
                  method = "hazard", t0 = t_0)

# Using the full data set, specify the effect modification column name as a string and tmle fits as 3rd and 4th args
surv_eff_mod(tmle_fit_0 = fit_noeff, tmle_fit_1 = fit_eff)

hoffmakl/tmleplus documentation built on Aug. 31, 2020, 4:33 p.m.