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.
We consider estimating average treatment effects (ATE) of a binary treatment in observational data when data‐driven variable selection is needed to select relevant covariates from a moderately large number of available covariates . To leverage covariates among predictive of the outcome for efficiency gain while using regularization to fit a parametric propensity score (PS) model, we consider a dimension reduction of based on fitting both working PS and outcome models using adaptive LASSO. A novel PS estimator, the Double‐index Propensity Score (DiPS), is proposed, in which the treatment status is smoothed over the linear predictors for from both the initial working models. The ATE is estimated by using the DiPS in a normalized inverse probability weighting estimator, which is found to maintain double robustness and also local semiparametric efficiency with a fixed number of covariates
- NSF-PAR ID:
- 10456582
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 76
- Issue:
- 3
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 767-777
- Size(s):
- ["p. 767-777"]
- Sponsoring Org:
- National Science Foundation
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