mets (version 1.3.2)

binreg: Binomial Regression for censored competing risks data

Description

Simple version of comp.risk function of timereg for just one time-point thus fitting the model $$P(T \leq t, \epsilon=1 | X ) = expit( X^T beta) $$

Usage

binreg(
  formula,
  data,
  cause = 1,
  time = NULL,
  beta = NULL,
  offset = NULL,
  weights = NULL,
  cens.weights = NULL,
  cens.model = ~+1,
  se = TRUE,
  kaplan.meier = TRUE,
  cens.code = 0,
  no.opt = FALSE,
  method = "nr",
  augmentation = NULL,
  ...
)

Arguments

formula

formula with outcome (see coxph)

data

data frame

cause

cause of interest (numeric variable)

time

time of interest

beta

starting values

offset

offsets for partial likelihood

weights

for score equations

cens.weights

censoring weights

cens.model

only stratified cox model without covariates

se

to compute se's based on IPCW

kaplan.meier

uses Kaplan-Meier for IPCW in contrast to exp(-Baseline)

cens.code

gives censoring code

no.opt

to not optimize

method

for optimization

augmentation

to augment binomial regression

...

Additional arguments to lower level funtions

Author

Thomas Scheike

Details

Based on binomial regresion IPCW response estimating equation: $$ X ( \Delta I(T \leq t, \epsilon=1 )/G_c(T_i-) - expit( X^T beta)) = 0 $$ for IPCW adjusted responses.

logitIPCW instead considers $$ X I(min(T_i,t) < G_i)/G_c(min(T_i ,t)) ( I(T \leq t, \epsilon=1 ) - expit( X^T beta)) = 0 $$ a standard logistic regression with weights that adjust for IPCW.

variance is based on $$ \sum w_i^2 $$ also with IPCW adjustment, and naive.var is variance under known censoring model.

Censoring model may depend on strata.

Examples

data(bmt)
# logistic regresion with IPCW binomial regression 
out <- binreg(Event(time,cause)~tcell+platelet,bmt,time=50)
summary(out)
predict(out,data.frame(tcell=c(0,1),platelet=c(1,1)),se=TRUE)

outs <- binreg(Event(time,cause)~tcell+platelet,bmt,time=50,cens.model=~strata(tcell,platelet))
summary(outs)

## glm with IPCW weights 
outl <- logitIPCW(Event(time,cause)~tcell+platelet,bmt,time=50)
summary(outl)

##########################################
### risk-ratio of different causes #######
##########################################
data(bmt)
bmt$id <- 1:nrow(bmt)
bmt$status <- bmt$cause
bmt$strata <- 1
bmtdob <- bmt
bmtdob$strata <-2
bmtdob <- dtransform(bmtdob,status=1,cause==2)
bmtdob <- dtransform(bmtdob,status=2,cause==1)
###
bmtdob <- rbind(bmt,bmtdob)
dtable(bmtdob,cause+status~strata)

cif1 <- cif(Event(time,cause)~+1,bmt,cause=1)
cif2 <- cif(Event(time,cause)~+1,bmt,cause=2)
bplot(cif1)
bplot(cif2,add=TRUE,col=2)

cifs1 <- binreg(Event(time,cause)~tcell+platelet+age,bmt,cause=1,time=50)
cifs2 <- binreg(Event(time,cause)~tcell+platelet+age,bmt,cause=2,time=50)
summary(cifs1)
summary(cifs2)

cifdob <- binreg(Event(time,status)~-1+factor(strata)+
	 tcell*factor(strata)+platelet*factor(strata)+age*factor(strata)
	 +cluster(id),bmtdob,cause=1,time=50,cens.model=~strata(strata)+cluster(id))
summary(cifdob)

expit  <- function(z) 1/(1+exp(-z)) 

riskratio <- function(p) {
  expit  <- function(z) 1/(1+exp(-z)) ## expit
  Z <- rbind(c(1,0,1,1,0,0,0,0), c(0,1,1,1,0,1,1,0))
  lp <- c(Z %*% p)
  p <- expit(lp)
  return(p[1]/p[2])
}

lava::estimate(cifdob,f=riskratio)