Effbinreg: Efficient IPCW for binary data

In mets: Analysis of Multivariate Event Times

Efficient IPCW for binary data

Description

Simple version of comp.risk function of timereg for just one time-point thus fitting the model

E(T ≤q t | X ) = expit( X^T beta)

Usage

Effbinreg(
  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,
  h = NULL,
  MCaugment = NULL,
  ...
)

Arguments

formula	formula with outcome (see coxph)
data	data frame
cause	cause of interest
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
h	h for estimating equation
MCaugment	iid of h and censoring model
...	Additional arguments to lower level funtions
model	exp or linear

Details

Based on binomial regresion IPCW response estimating equation:

X ( Δ (T ≤q t)/G_c(T_i-) - expit( X^T beta)) = 0

for IPCW adjusted responses.

Based on binomial regresion IPCW response estimating equation:

h(X) X ( Δ (T ≤q t)/G_c(T_i-) - expit( X^T beta)) = 0

for IPCW adjusted responses where $h$ is given as an argument together with iid of censoring with h. By using appropriately the h argument we can also do the efficient IPCW estimator estimator this works the prepsurv and prepcif for survival or competing risks data. In this case also the censoring martingale should be given for variance calculation and this also comes out of the prepsurv or prepcif functions. (Experimental version at this stage).

Variance is based on

∑ w_i^2

also with IPCW adjustment, and naive.var is variance under known censoring model.

Censoring model may depend on strata.

Author(s)

Thomas Scheike

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