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Meta-analysis with zero-event studies: a comparative study with application to COVID-19 data.

Jia-Jin Wei1, En-Xuan Lin2, Jian-Dong Shi1

  • 1Department of Mathematics, Hong Kong Baptist University, Hong Kong, China.

Military Medical Research
|July 4, 2021
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Summary

This study introduces a new continuity correction method for meta-analysis with zero events, recommending it for few studies. Generalized linear mixed models are best for many studies, and double-zero-event data should not be excluded.

Keywords:
Continuity correctionCoronavirus disease 2019 dataMeta-analysisRelative riskZero-event studies

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research

Background:

  • Meta-analysis synthesizes evidence from independent studies, crucial for clinical research with binary outcomes.
  • Zero events in one or both groups during meta-analysis can lead to statistical challenges.
  • Accurate synthesis of evidence is vital for informed clinical decision-making.

Purpose of the Study:

  • To compare continuity correction methods and generalized linear mixed models (GLMMs) for meta-analysis with zero events.
  • To introduce a novel continuity correction method for relative risk estimation in meta-analysis.
  • To address the impact of double-zero-event studies on effect size estimation.

Main Methods:

  • A comparative study evaluating four continuity correction methods and GLMMs using relative risk as the effect size.
  • Development and introduction of a new continuity correction technique for relative risk.
  • Simulation studies and reanalysis of COVID-19 data to assess method performance.

Main Results:

  • The new continuity correction method shows good performance (mean squared error) with few studies.
  • Generalized linear mixed models (GLMMs) demonstrate superior performance when the number of studies is large.
  • Double-zero-event studies significantly influence the estimated mean effect size, as shown in COVID-19 data.

Conclusions:

  • Recommend the new method for meta-analyses with few studies and zero events.
  • Suggest using GLMMs for meta-analyses involving a large number of studies.
  • Advise against excluding double-zero-event studies, as they contain valuable information.