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Activity Number: 124 - Causal Inference and Observational Health Policy Studies
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #327107 Presentation
Title: Instrumental Variable Methods for Comparing Cancer Survival Outcomes
Author(s): Fei Wan* and Dylan Small and Nandita Mitra
Companies: University of Arkansas for Medical Sciences and University of Pennsylvania and University of Pennsylvania
Keywords: Instrumental variables; Two stage residual inclusion; Unmeasured confounding; Survival outcome

Observational studies are commonly used by cancer researchers to evaluate the relative effectiveness of different treatment options. However, confounding due to unmeasured or unknown variables can pose a serious issue when estimating the comparative effectiveness of therapies from observational data. To address this issue of unmeasured confounding, the IV approach using two-stage residual inclusion (2SRI) has become a common analytic tool in studies of cancer therapies where the outcome of interest is overall or cancer-specific survival. However, despite its popularity, a compelling theoretical rationale has not been postulated nor have the limitations underlying the use of 2SRI in the context of survival outcomes been carefully laid out. We show that the previous conclusion on the consistency of 2SRI in proportional hazards model relies on the unrealistic assumption that the effects of unmeasured confounders on treatment and outcome are proportional. Given a perfect instrumental variable, extension of 2SRI to proportional hazards model is generally biased. We present a simple approach of assessing the bias of 2SRI as an omitted-variable-bias problem.

Authors who are presenting talks have a * after their name.

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