mediatorSurv {mets} | R Documentation |
Mediation analysis in survival context with robust standard errors taking the weights into account via influence function computations. Mediator and exposure must be factors. This is based on numerical derivative wrt parameters for weighting. See vignette for more examples.
mediatorSurv( survmodel, weightmodel, data = data, wdata = wdata, id = "id", silent = TRUE, ... )
survmodel |
with mediation model (binreg, aalenMets, phreg) |
weightmodel |
mediation model |
data |
for computations |
wdata |
weighted data expansion for computations |
id |
name of id variable, important for SE computations |
silent |
to be silent |
... |
Additional arguments to survival model |
Thomas Scheike
n <- 400 dat <- kumarsimRCT(n,rho1=0.5,rho2=0.5,rct=2,censpar=c(0,0,0,0), beta = c(-0.67, 0.59, 0.55, 0.25, 0.98, 0.18, 0.45, 0.31), treatmodel = c(-0.18, 0.56, 0.56, 0.54),restrict=1) dfactor(dat) <- dnr.f~dnr dfactor(dat) <- gp.f~gp drename(dat) <- ttt24~"ttt24*" dat$id <- 1:n dat$ftime <- 1 weightmodel <- fit <- glm(gp.f~dnr.f+preauto+ttt24,data=dat,family=binomial) wdata <- medweight(fit,data=dat) ### fitting models with and without mediator aaMss2 <- binreg(Event(time,status)~gp+dnr+preauto+ttt24+cluster(id),data=dat,time=50,cause=2) aaMss22 <- binreg(Event(time,status)~dnr+preauto+ttt24+cluster(id),data=dat,time=50,cause=2) ### estimating direct and indirect effects (under strong strong assumptions) aaMss <- binreg(Event(time,status)~dnr.f0+dnr.f1+preauto+ttt24+cluster(id), data=wdata,time=50,weights=wdata$weights,cause=2) ## to compute standard errors , requires numDeriv library(numDeriv) ll <- mediatorSurv(aaMss,fit,data=dat,wdata=wdata) summary(ll) ## not run bootstrap (to save time) ## bll <- BootmediatorSurv(aaMss,fit,data=dat,k.boot=500)