Abstract:
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We develop an inferential framework for estimating the causal effect among "exposed" subjects on a time-to-event outcome, based on multiple data sources and censored outcome information. We conceptualize a hypothetical point exposure study where subjects are enrolled and allowed to select their own exposure. Using information from two data sources (one for exposed subjects and one for non-exposed subjects with multiple examination times), we describe a process of manufacturing a dataset that closely mimics this hypothetical study. The identification of the causal effect relies on a no unmeasured confounding assumption based on covariates available at exposure selection and a non-informative censoring assumption. Estimation proceeds by fitting separate proportional hazards regression models for exposed and non-exposed subjects using the manufactured dataset and using G-computation to estimate, for exposed subjects, the distributions of time-to-event under exposure and non-exposure. Using these estimated distributions, we compute a parsimonious measure of the causal effect of interest. We also present a simulation study and a real data illustration.
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