On finite-population Bayesian inferences for 2K factorial designs with binary outcomes

Inspired by the pioneering work of Rubin [Bayesian inference for causal effects: the role of randomization. Ann Stat. 1978;6:34-58], we employ the potential outcomes framework to develop a finite-population Bayesian causal inference framework for randomized controlled factorial designs with binary o...

Full description

Saved in:
Bibliographic Details
Published in:Journal of statistical computation and simulation 2019-03, Vol.89 (5), p.927-945
Main Author: Lu, Jiannan
Format: Article
Language:English
Subjects:
Bayesian analysis
Factorial effect
frequentist-Bayes reconciliation
Medical research
potential outcome
Randomization
randomization-based inference
sensitivity analysis
Statistical inference
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Inspired by the pioneering work of Rubin [Bayesian inference for causal effects: the role of randomization. Ann Stat. 1978;6:34-58], we employ the potential outcomes framework to develop a finite-population Bayesian causal inference framework for randomized controlled factorial designs with binary outcomes, which are common in medical research. As demonstrated by simulated and empirical examples, the proposed framework corrects the well-known variance over-estimation issue of the classic 'Neymanian' inference framework, under various settings.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2019.1574793