Causal Effect Among the Exposed: Multiple Data Sources and Censored Outcomes

• Activity Number: 296
• Type: Topic Contributed
• Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Parichoy Pal Choudhury* and Daniel Scharfstein and Ivan Diaz and Chris McMahan and Xun Luo and Allan Massie and Dorry Segev
• Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Clemson University and Johns Hopkins School of Medicine and Johns Hopkins School of Medicine and Johns Hopkins School of Medicine
• Keywords: causal effect ; time-to-event ; interval censoring ; multiple studies

Abstract

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.