mets (version 1.3.5)

resmeanATE: 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

Author

Thomas Scheike

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.

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)