Effbinreg: Efficient IPCW for binary data

View source: R/efficient-binreg.R

EffbinregR Documentation

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 Oct. 2, 2022, 5:06 p.m.