Journal
BIOSTATISTICS
Volume 4, Issue 4, Pages 495-512Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/4.4.495
Keywords
Dirichlet process prior; identifiability; MCHC; non-parametric Bayes; selection model; sensitivity analysis
Funding
- NCI NIH HHS [CA85295, R01 CA085295-02, R01 CA085295] Funding Source: Medline
- NICHD NIH HHS [HD38209] Funding Source: Medline
- NIMH NIH HHS [MH56639] Funding Source: Medline
- PHS HHS [A132475] Funding Source: Medline
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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 asssumptions 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.
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