Simple version of comp.risk function of timereg for just one time-point thus fitting the model $$E(T \leq t | X ) = expit( X^T beta) $$
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: $$ X ( \Delta (T \leq t)/G_c(T_i-) - expit( X^T beta)) = 0 $$ for IPCW adjusted responses.
Based on binomial regresion IPCW response estimating equation: $$ h(X) X ( \Delta (T \leq t)/G_c(T_i-) - expit( X^T beta)) = 0 $$ 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 $$ \sum w_i^2 $$ also with IPCW adjustment, and naive.var is variance under known censoring model.
Censoring model may depend on strata.