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A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models
Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150008/ https://www.ncbi.nlm.nih.gov/pubmed/34054640 http://dx.doi.org/10.3389/fpsyg.2021.614236 |
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author | König, Christoph |
author_facet | König, Christoph |
author_sort | König, Christoph |
collection | PubMed |
description | Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions. |
format | Online Article Text |
id | pubmed-8150008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81500082021-05-27 A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models König, Christoph Front Psychol Psychology Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions. Frontiers Media S.A. 2021-05-11 /pmc/articles/PMC8150008/ /pubmed/34054640 http://dx.doi.org/10.3389/fpsyg.2021.614236 Text en Copyright © 2021 König. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology König, Christoph A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models |
title | A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models |
title_full | A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models |
title_fullStr | A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models |
title_full_unstemmed | A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models |
title_short | A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models |
title_sort | similarity-weighted informative prior distribution for bayesian multiple regression models |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150008/ https://www.ncbi.nlm.nih.gov/pubmed/34054640 http://dx.doi.org/10.3389/fpsyg.2021.614236 |
work_keys_str_mv | AT konigchristoph asimilarityweightedinformativepriordistributionforbayesianmultipleregressionmodels AT konigchristoph similarityweightedinformativepriordistributionforbayesianmultipleregressionmodels |