resmeanATE {mets}

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)