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Comparative effectiveness research often involves evaluating the differencesin the risks of an event of interest between two or more treatments usingobservational data. Often, the post-treatment outcome of interest is whetherthe event happens within a pre-specified time window, which leads to a binaryoutcome. One source of bias for estimating the causal treatment effect is thepresence of confounders, which are usually controlled using propensityscore-based methods. An additional source of bias is right-censoring, whichoccurs when the information on the outcome of interest is not completelyavailable due to dropout, study termination, or treatment switch before theevent of interest. We propose an inverse probability weighted regression-basedestimator that can simultaneously handle both confounding and right-censoring,calling the method CIPWR, with the letter C highlighting the censoringcomponent. CIPWR estimates the average treatment effects by averaging thepredicted outcomes obtained from a logistic regression model that is fittedusing a weighted score function. The CIPWR estimator has a double robustnessproperty such that estimation consistency can be achieved when either the modelfor the outcome or the models for both treatment and censoring are correctlyspecified. We establish the asymptotic properties of the CIPWR estimator forconducting inference, and compare its finite sample performance with that ofseveral alternatives through simulation studies. The methods under comparisonare applied to a cohort of prostate cancer patients from an insurance claimsdatabase for comparing the adverse effects of four candidate drugs for advancedstage prostate cancer.

An Inverse Probability Weighted Regression Method that Accounts for  Right-censoring for Causal Inference with Multiple Treatments and a Binary  Outcome