title: A Bayesian approach to estimating causal vaccine effects on binary post-infection outcomes.
abstract: To estimate causal effects of vaccine on post-infection outcomes, Hudgens and Halloran (2006) defined a post-infection causal vaccine efficacy estimand VEI based on the principal stratification framework. They also derived closed forms for the maximum likelihood estimators of the causal estimand under some assumptions. Extending their research, we propose a Bayesian approach to estimating the causal vaccine effects on binary post-infection outcomes. The identifiability of the causal vaccine effect VEI is discussed under different assumptions on selection bias. The performance of the proposed Bayesian method is compared with the maximum likelihood method through simulation studies and two case studies - a clinical trial of a rotavirus vaccine candidate and a field study of pertussis vaccination. For both case studies, the Bayesian approach provided similar inference as the frequentist analysis. However, simulation studies with small sample sizes suggest that the Bayesian approach provides smaller bias and shorter confidence interval length.
authors.affiliation: Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455, U.S.A.
authors.forename: Jincheng
authors.initials: J
authors.lastname: Zhou
authors.affiliation: Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455, U.S.A.
authors.forename: Haitao
authors.initials: H
authors.lastname: Chu
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A.
authors.forename: Michael G
authors.initials: MG
authors.lastname: Hudgens
authors.affiliation: Center for Inference and Dynamics of Infectious Disease, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, U.S.A.|Department of Biostatistics, University of Washington, Seattle, WA 98195, U.S.A.
authors.forename: M Elizabeth
authors.initials: ME
authors.lastname: Halloran
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D015984:Causality; D003198:Computer Simulation; D006801:Humans; D017053:Infection Control / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000706:statistics & numerical data; D010567:Pertussis Vaccine / Q000494:pharmacology; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D022243:Rotavirus Vaccines / Q000494:pharmacology; D015983:Selection Bias; D016896:Treatment Outcome; D014612:Vaccines / Q000494:pharmacology*
chemical_list: D010567:Pertussis Vaccine; D022243:Rotavirus Vaccines; D014612:Vaccines
keywords: Bayesian methods; causal inferences; principal stratification; vaccine effects