  Comparative effectiveness research often involves evaluating the differences
in the risks of an event of interest between two or more treatments using
observational data. Often, the post-treatment outcome of interest is whether
the event happens within a pre-specified time window, which leads to a binary
outcome. One source of bias for estimating the causal treatment effect is the
presence of confounders, which are usually controlled using propensity
score-based methods. An additional source of bias is right-censoring, which
occurs when the information on the outcome of interest is not completely
available due to dropout, study termination, or treatment switch before the
event of interest. We propose an inverse probability weighted regression-based
estimator that can simultaneously handle both confounding and right-censoring,
calling the method CIPWR, with the letter C highlighting the censoring
component. CIPWR estimates the average treatment effects by averaging the
predicted outcomes obtained from a logistic regression model that is fitted
using a weighted score function. The CIPWR estimator has a double robustness
property such that estimation consistency can be achieved when either the model
for the outcome or the models for both treatment and censoring are correctly
specified. We establish the asymptotic properties of the CIPWR estimator for
conducting inference, and compare its finite sample performance with that of
several alternatives through simulation studies. The methods under comparison
are applied to a cohort of prostate cancer patients from an insurance claims
database for comparing the adverse effects of four candidate drugs for advanced
stage prostate cancer.
