On the estimation of average treatment effects with right-censored time to event outcome and competing risks.
We are interested in the estimation of average treatment effects based on right-censored data of an observational study. We focus on causal inference of differences between t-year absolute event risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, censoring, and treatment distribution conditional on auxiliary baseline covariates. We use the functional delta method to show that these estimators are regular asymptotically linear estimators and estimate their variances based on estimates of their influence functions. In empirical studies, we assess the robustness of the estimators and the coverage of confidence intervals. The methods are further illustrated using data from a Danish registry study.
['Ozenne|Brice Maxime Hugues|BMH|0000-0001-9694-2956', 'Scheike|Thomas Harder|TH|', 'Staerk|Laila|L|', 'Gerds|Thomas Alexander|TA|']
D001699:Biometry D006801:Humans D064887:Observational Studies as Topic D012044:Regression Analysis D012306:Risk D013997:Time Factors
Cox regression model hazard ratio probabilistic index relative risk survival analysis