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Meta-analysis Using Flexible Random-effects Distribution Models.

Hisashi Noma1, Kengo Nagashima2, Shogo Kato3

  • 1Department of Data Science, The Institute of Statistical Mathematics.

Journal of Epidemiology
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

The common normal distribution assumption in random-effects meta-analysis may lead to incorrect conclusions. Flexible distribution models offer more accurate results for systematic reviews.

Keywords:
flexible probability distributionmeta-analysismodel inadequacypredictive distributionrandom-effects model

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • The normal distribution assumption is widely used in random-effects meta-analysis for its simplicity.
  • This assumption may be too restrictive and impact the reliability of systematic reviews.

Purpose of the Study:

  • To evaluate the suitability of the normal distribution assumption in random-effects meta-analysis.
  • To introduce flexible random-effects distribution models for more accurate meta-analysis.

Main Methods:

  • Demonstrated unsuitability of normal distribution with two real-world evidence examples.
  • Proposed new random-effects meta-analysis methods using five flexible distributions (skew normal, skew t, asymmetric Subbotin, Jones-Faddy, sinh-arcsinh).
  • Developed the 'flexmeta' statistical package for implementing these methods.

Main Results:

  • Meta-analysis results were significantly altered when using flexible distribution models.
  • Conclusions from systematic reviews could be influenced by the choice of distribution model.

Conclusions:

  • The restrictive normal distribution assumption can lead to misleading conclusions in meta-analysis.
  • Proposed flexible methods enhance precision and reliability of conclusions in systematic reviews.