Borrowing strength and borrowing index for Bayesian hierarchical models

Ganggang Xu, Huirong Zhu and J. Jack Lee

Computational Statistics & Data Analysis, 2020, vol. 144, issue C

Abstract: A novel borrowing strength measure and an overall borrowing index to characterize the strength of borrowing behaviors among subgroups are proposed for a given Bayesian hierarchical model. The constructions of the proposed indexes are based on the Mallow’s distance and can be easily computed using MCMC samples for univariate or multivariate posterior distributions. Consequently, the proposed indexes can serve as meaningful and useful exploratory tools to better understand the roles played by the priors in a hierarchical model, including their influences on the posteriors that are used to make statistical inferences. These relationships are otherwise ambiguous. The proposed methods can be applied to both the continuous and binary outcome variables. Furthermore, the proposed approach can be easily adapted to various settings of clinical trials, where Bayesian hierarchical models are deem appropriate. The effectiveness of the proposed method is illustrated using extensive simulation studies and a real data example.

Keywords: Bayesian hierarchical model; Borrowing index; Borrowing strength; Clinical trials; Mallow’s distance (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947319302567
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302567

DOI: 10.1016/j.csda.2019.106901

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().