Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes.
In randomized studies with missing outcomes, non-identifiable assumptions are required to hold for valid data analysis. As a result, statisticians have been advocating the use of sensitivity analysis to evaluate the effect of varying assumptions on study conclusions. While this approach may be useful in assessing the sensitivity of treatment comparisons to missing data assumptions, it may be dissatisfying to some researchers/decision makers because a single summary is not provided. In this paper, we present a fully Bayesian methodology that allows the investigator to draw a 'single' conclusion by formally incorporating prior beliefs about non-identifiable, yet interpretable, selection bias parameters. Our Bayesian model provides robustness to prior specification of the distributional form of the continuous outcomes.
Scharfstein
Daniel O
DO
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA. dscharf@jhsph.edu
Daniels
Michael J
MJ
Robins
James M
JM
D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy / Q000453:epidemiology; D019380:Anti-HIV Agents / Q000008:administration & dosage; D001499:Bayes Theorem; D018791:CD4 Lymphocyte Count; D003627:Data Interpretation, Statistical; D016049:Didanosine / Q000008:administration & dosage; D004311:Double-Blind Method; D004359:Drug Therapy, Combination; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D015983:Selection Bias; D018709:Statistics, Nonparametric; D016896:Treatment Outcome; D015215:Zidovudine / Q000008:administration & dosage
D019380:Anti-HIV Agents; D015215:Zidovudine; D016049:Didanosine