Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
#dev="png",
comment = "#>"
)
fullVignette <- Sys.getenv("_R_FULL_VIGNETTE_") %in% c("1","TRUE")
library(mets)
## -----------------------------------------------------------------------------
library(mets)
runb <- 0
options(warn=-1)
set.seed(1000) # to control output in simulatins for p-values below.
n <- 200; k.boot <- 10;
dat <- kumarsimRCT(n,rho1=0.5,rho2=0.5,rct=2,censpar=c(0,0,0,0),
beta = c(-0.67, 0.59, 0.55, 0.25, 0.98, 0.18, 0.45, 0.31),
treatmodel = c(-0.18, 0.56, 0.56, 0.54),restrict=1)
dfactor(dat) <- dnr.f~dnr
dfactor(dat) <- gp.f~gp
drename(dat) <- ttt24~"ttt24*"
dat$id <- 1:n
dat$ftime <- 1
## -----------------------------------------------------------------------------
weightmodel <- fit <- glm(gp.f~dnr.f+preauto+ttt24,data=dat,family=binomial)
wdata <- medweight(fit,data=dat)
aaMss2 <- binreg(Event(time,status)~gp+dnr+preauto+ttt24+cluster(id),data=dat,time=50,cause=2)
summary(aaMss2)
aaMss22 <- binreg(Event(time,status)~dnr+preauto+ttt24+cluster(id),data=dat,time=50,cause=2)
summary(aaMss22)
## ---- label=multiplemodels----------------------------------------------------
### binomial regression ###########################################################
aaMss <- binreg(Event(time,status)~dnr.f0+dnr.f1+preauto+ttt24+cluster(id),data=wdata,time=50,weights=wdata$weights,cause=2)
summary(aaMss)
ll <- mediatorSurv(aaMss,fit,data=dat,wdata=wdata)
summary(ll)
if (runb>0) { bll <- BootmediatorSurv(aaMss,fit,data=dat,k.boot=k.boot); summary(bll)}
### lin-ying model ################################################################
aaMss <- aalenMets(Surv(time/100,status==2)~dnr.f0+dnr.f1+preauto+ttt24+cluster(id),data=wdata,weights=wdata$weights)
ll <- mediatorSurv(aaMss,fit,data=dat,wdata=wdata)
summary(ll)
if (runb>0) { bll <- BootmediatorSurv(aaMss,fit,data=dat,k.boot=k.boot); summary(bll)}
### cox model ###############################################################################
aaMss <- phreg(Surv(time,status==2)~dnr.f0+dnr.f1+preauto+ttt24+cluster(id),data=wdata,weights=wdata$weights)
summary(aaMss)
ll <- mediatorSurv(aaMss,fit,data=dat,wdata=wdata)
summary(ll)
if (runb>0) { bll <- BootmediatorSurv(aaMss,fit,data=dat,k.boot=k.boot); summary(bll)}
### Fine-Gray #############################################################3
aaMss <- cifreg(Event(time,status)~dnr.f0+dnr.f1+preauto+ttt24+cluster(id),data=wdata,weights=wdata$weights,propodds=NULL,cause=2)
summary(aaMss)
ll <- mediatorSurv(aaMss,fit,data=dat,wdata=wdata)
summary(ll)
if (runb>0) { bll <- BootmediatorSurv(aaMss,fit,data=dat,k.boot=k.boot); summary(bll)}
### logit model #############################################################3
aaMss <- cifreg(Event(time,status)~dnr.f0+dnr.f1+preauto+ttt24+cluster(id),data=wdata,weights=wdata$weights,cause=2)
summary(aaMss)
ll <- mediatorSurv(aaMss,fit,data=dat,wdata=wdata)
summary(ll)
if (runb>0) { bll <- BootmediatorSurv(aaMss,fit,data=dat,k.boot=k.boot); summary(bll)}
### binomial outcome ############################
aaMss <- binreg(Event(ftime,status)~dnr.f0+dnr.f1+preauto+ttt24+cluster(id),data=wdata,time=50,weights=wdata$weights,cens.weights=1,cause=2)
summary(aaMss)
ll <- mediatorSurv(aaMss,fit,data=dat,wdata=wdata)
summary(ll)
if (runb>0) { bll <- BootmediatorSurv(aaMss,fit,data=dat,k.boot=k.boot); summary(bll)}
## ---- label=multinom, cache=TRUE, eval=fullVignette---------------------------
# data(tTRACE)
# dcut(tTRACE) <- ~.
#
# weightmodel <- fit <- mlogit(wmicat.4 ~agecat.4+vf+chf,data=tTRACE,family=binomial)
# wdata <- medweight(fit,data=tTRACE)
#
# aaMss <- binreg(Event(time,status)~agecat.40+ agecat.41+ vf+chf+cluster(id),data=wdata,time=7,weights=wdata$weights,cause=9)
# summary(aaMss)
# MultMed <- mediatorSurv(aaMss,fit,data=tTRACE,wdata=wdata)
## ----results="hide", echo=FALSE-----------------------------------------------
## To save time building the vignettes on CRAN, we cache time consuming computations
if (fullVignette) {
MultMed[c('iid','iid.w','iid.surv')] <- NULL
saveRDS(MultMed, "data/MultMed.rds")
} else {
MultMed <- readRDS("data/MultMed.rds")
}
## -----------------------------------------------------------------------------
summary(MultMed)
## -----------------------------------------------------------------------------
sessionInfo()
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