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Activity Number: 61 - Approaches for Modeling Clustered and Longitudinal Data
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #310952
Title: Semiparametric Quasi-Bayesian Bootstrap Procedure for Dichotomous Outcome
Author(s): Yiying Liu* and Jeffery M Albert
Companies: Case Western Reserve University and Case Western Reserve University
Keywords: Bootstrap; Quasi-Bayesian; Semiparametric; Proportion Estimation

We propose a new resampling procedure, a variation of the Quasi-Bayesian bootstrap, for inference in the case of a single population proportion and for comparing two independent population proportions. As a semiparametric technique, the Quasi-Bayesian bootstrap offers an attractive alternative to the existing nonparametric and parametric bootstrap procedures. In our approach, we consider proportions as beta-distributed random variables, with parameter values determined using sample proportions and sample size. Variance estimates and confidence intervals are obtained in a two-stage Monte Carlo approach in which proportions are drawn from betas and samples (or sample proportions) then obtained given the drawn proportions. The idea is to account for sampling variability, and to reduce risk in the estimation of variances, particularly for small size samples (n?20). We present results of simulation studies showing that the Quasi-Bayesian variance estimators and confidence intervals outperform those of standard approaches based on mean square error (MSE) and confidence interval width, respectively. The method is applied to data from a cohort study of dental caries in children.

Authors who are presenting talks have a * after their name.

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