A Bayesian approach for instrumental variable analysis with censored time-to-event outcome.
Instrumental variable (IV) analysis has been widely used in economics, epidemiology, and other fields to estimate the causal effects of covariates on outcomes, in the presence of unobserved confounders and/or measurement errors in covariates. However, IV methods for time-to-event outcome with censored data remain underdeveloped. This paper proposes a Bayesian approach for IV analysis with censored time-to-event outcome by using a two-stage linear model. A Markov chain Monte Carlo sampling method is developed for parameter estimation for both normal and non-normal linear models with elliptically contoured error distributions. The performance of our method is examined by simulation studies. Our method largely reduces bias and greatly improves coverage probability of the estimated causal effect, compared with the method that ignores the unobserved confounders and measurement errors. We illustrate our method on the Women's Health Initiative Observational Study and the Atherosclerosis Risk in Communities Study.
Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA 90095-1772, U.S.A.
Gang
G
Li
Xuyang
X
Lu
D000704:Analysis of Variance; D050197:Atherosclerosis / Q000209:etiology; D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008390:Markov Chains; D009010:Monte Carlo Method; D064887:Observational Studies as Topic / Q000706:statistics & numerical data; D012307:Risk Factors; D016387:Women's Health / Q000706:statistics & numerical data
Bayesian method; Mendelian randomization; accelerated failure time model; instrumental variable analysis; measurement error; time-to-event outcome