A Bayesian nonparametric test of significance chasing biases

A Bayesian nonparametric test of significance chasing biases There is a growing concern that much of the published research literature is distorted by the pursuit of statistically significant results. In a seminal article, Ioannidis and Trikalinos (2007, Clinical Trials) proposed an omnibus (I&T) test for significance chasing (SC) biases. This test compares the observed number of studies that report statistically significant results, against their expected number based on study power, assuming a common effect size across studies. The current article extends this approach by developing a Bayesian nonparametric (BNP) meta‐regression model and test of SC bias, which can diagnose bias at the individual study level. This new BNP test is based on a flexible model of the predictive distribution of study power, conditionally on study‐level covariates which account for study diversity, including diversity due to heterogeneous effect sizes across studies. A test of SC bias proceeds by comparing each study's significant outcome report indicator against its estimated posterior predictive distribution of study power, conditionally on the study's covariates. The BNP model and SC bias test are illustrated through the analyses of 3 meta‐analytic data sets and through a simulation study. Software code for the BNP model and test, and the data sets, are provided as Supporting Information. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research Synthesis Methods Wiley

A Bayesian nonparametric test of significance chasing biases

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1759-2879
eISSN
1759-2887
D.O.I.
10.1002/jrsm.1269
Publisher site
See Article on Publisher Site

Abstract

There is a growing concern that much of the published research literature is distorted by the pursuit of statistically significant results. In a seminal article, Ioannidis and Trikalinos (2007, Clinical Trials) proposed an omnibus (I&T) test for significance chasing (SC) biases. This test compares the observed number of studies that report statistically significant results, against their expected number based on study power, assuming a common effect size across studies. The current article extends this approach by developing a Bayesian nonparametric (BNP) meta‐regression model and test of SC bias, which can diagnose bias at the individual study level. This new BNP test is based on a flexible model of the predictive distribution of study power, conditionally on study‐level covariates which account for study diversity, including diversity due to heterogeneous effect sizes across studies. A test of SC bias proceeds by comparing each study's significant outcome report indicator against its estimated posterior predictive distribution of study power, conditionally on the study's covariates. The BNP model and SC bias test are illustrated through the analyses of 3 meta‐analytic data sets and through a simulation study. Software code for the BNP model and test, and the data sets, are provided as Supporting Information.

Journal

Research Synthesis MethodsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

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