resmeanATE: Average Treatment effect for Restricted Mean for censored...

In mets: Analysis of Multivariate Event Times

Average Treatment effect for Restricted Mean for censored competing risks data using IPCW

Description

Under the standard causal assumptions we can estimate the average treatment effect E(Y(1) - Y(0)). We need Consistency, ignorability ( Y(1), Y(0) indep A given X), and positivity.

Usage

resmeanATE(
  formula,
  data,
  outcome = c("rmst", "rmst-cause"),
  model = "exp",
  ...
)

Arguments

formula	formula with 'Event' outcome
data	data-frame
outcome	"rmst"=E( min(T, t) | X) , or "rmst-cause"=E( I(epsilon==cause) ( t - mint(T,t)) ) | X)
model	possible exp model for relevant mean model that is exp(X^t beta)
...	Additional arguments to pass to binregATE

Details

The first covariate in the specification of the competing risks regression model must be the treatment effect that is a factor. If the factor has more than two levels then it uses the mlogit for propensity score modelling. We consider the outcome mint(T;tau) or I(epsion==cause1)(t- min(T;t)) that gives years lost due to cause "cause".

Estimates the ATE using the the standard binary double robust estimating equations that are IPCW censoring adjusted.

Author(s)

Thomas Scheike

Examples

library(mets); data(bmt); bmt$event <- bmt$cause!=0; dfactor(bmt) <- tcell~tcell
out <- resmeanATE(Event(time,event)~tcell+platelet,data=bmt,time=40,treat.model=tcell~platelet)
summary(out)

out1 <- resmeanATE(Event(time,cause)~tcell+platelet,data=bmt,cause=1,outcome="rmst-cause",
                   time=40,treat.model=tcell~platelet)
summary(out1)

mets documentation built on Jan. 17, 2023, 5:12 p.m.