Meta-Analysis and Sparse-Data Bias

David B. Richardson, Stephen R. Cole, Rachael K. Ross, Charles Poole, Haitao Chu, Alexander P. Keil

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Meta-analyses are undertaken to combine information from a set of studies, often in settings where some of the individual study-specific estimates are based on relatively small study samples. Finite sample bias may occur when maximum likelihood estimates of associations are obtained by fitting logistic regression models to sparse data sets. Here we show that combining information from small studies by undertaking a meta-analytical summary of logistic regression estimates can propagate such sparse-data bias. In simulations, we illustrate 2 challenges encountered in meta-analyses of logistic regression results in settings of sparse data: 1) bias in the summary meta-analytical result and 2) confidence interval coverage that can worsen rather than improve, in terms of being less than nominal, as the number of studies in the meta-analysis increases.

Original languageEnglish (US)
Pages (from-to)336-340
Number of pages5
JournalAmerican journal of epidemiology
Volume190
Issue number2
DOIs
StatePublished - Sep 25 2020

Bibliographical note

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords

  • cohort studies
  • logistic regression
  • meta-analysis
  • regression analysis

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