resmeanATE {mets} | R Documentation |
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, model = "exp", ...)
Arguments
formula |
formula with 'Event' outcome |
data |
data-frame |
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" depending on the number of causes. The default model is the exp(X^ beta) and otherwise a linear model is used.
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,time=40,
treat.model=tcell~platelet)
summary(out1)