Simple version of comp.risk function of timereg for just one time-point thus fitting the model $$E(T \leq t | X ) = expit( X^T beta) $$
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,
...
)formula with outcome (see coxph)
data frame
cause of interest
time of interest
starting values
offsets for partial likelihood
for score equations
censoring weights
only stratified cox model without covariates
to compute se's based on IPCW
uses Kaplan-Meier for IPCW in contrast to exp(-Baseline)
gives censoring code
to not optimize
for optimization
to augment binomial regression
h for estimating equation
iid of h and censoring model
Additional arguments to lower level funtions
exp or linear
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.