mediatorSurv: Mediation analysis in survival context

In mets: Analysis of Multivariate Event Times

Mediation analysis in survival context

Description

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.

Usage

mediatorSurv(
  survmodel,
  weightmodel,
  data = data,
  wdata = wdata,
  id = "id",
  silent = TRUE,
  ...
)

Arguments

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

Author(s)

Thomas Scheike

Examples

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)

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