A Bayesian Approach for Instrumental Variable Analysis with Time-to-Event Outcome

• Activity Number: 562
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Xuyang Lu*+ and Gang Li
• Companies: University of California at Los Angeles and University of California at Los Angeles
• Keywords: Instrumental variable analysis ; Time-to-event outcome ; Accelerated failure time model ; Bayesian methods ; Markov Chain Monte Carlo

Abstract

The method of instrumental variable (IV) analysis is widely used in economics, epidemiology and other fields to estimate the effects of covariates on outcomes in the presence of unknown confounding factors. Consistent estimation of the effect has been developed when the outcome is continuous, while methods for binary outcome produce inconsistent estimation. Identifiability problem of IV analysis with binary outcome is discussed. We further extend the IV method to time-to-event outcome and propose a parametric Bayesian approach based on accelerating failure time models. Markov Chain Monte Carlo sampling methods are developed based on a reduced-form of the model. Performance of the model is examined by simulation studies, and we apply the model to a real data from the Women's Health Initiative Observational Study with age of type 2 diabetes diagnosis as time-to-event outcome.