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How reliable are the multiple comparison methods for odds ratio?

Ayfer Ezgi Yilmaz1

  • 1Department of Statistics, Hacettepe University, Ankara, Turkey.

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|August 29, 2022
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Summary
This summary is machine-generated.

This study evaluates the accuracy of homogeneity tests for odds ratios in meta-analysis, especially for COVID-19 data. It assesses multiple comparison tests and homogeneity tests to ensure reliable results when data is heterogeneous.

Keywords:
COVID-19Homogeneity of odds ratiosmeta-analysismultiple comparisonsstatistical powertype-I error

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Homogeneity tests of odds ratios are crucial preliminary steps in meta-analysis for clinical trials and epidemiological studies.
  • Interpreting odds ratios and meta-analysis are common for discussing COVID-19 severity in relation to various factors.
  • When heterogeneity exists, multiple comparison procedures are needed to identify specific differing subgroups, rather than just a common odds ratio.

Purpose of the Study:

  • To assess the accuracy and reliability of homogeneity of odds ratio tests for multiple comparisons.
  • To evaluate these methods when odds ratios are heterogeneous at the omnibus level.
  • To compare the performance of recently proposed multiple comparison tests and homogeneity tests with various adjustment methods.

Main Methods:

  • Considered three recently proposed multiple comparison tests.
  • Evaluated four homogeneity of odds ratios tests.
  • Utilized six adjustment methods to control the type-I error rate.
  • Conducted a simulation study to assess powers and type-I error rates.
  • Applied methods to COVID-19 severity data associated with diabetes on a country-by-country basis.

Main Results:

  • The study discusses the reliability and accuracy of the evaluated methods using COVID-19 data.
  • A simulation study was performed to assess the powers and type-I error rates of the tests.
  • Findings provide insights into the performance of different statistical approaches under heterogeneity.

Conclusions:

  • The research highlights the importance of accurate homogeneity testing in meta-analysis, particularly for complex health data like COVID-19.
  • Reliable methods are essential for identifying specific risk factors or differing outcomes among subgroups.
  • The study contributes to understanding the performance of statistical tests when dealing with heterogeneous odds ratios.