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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Likelihood-Based Random-Effect Meta-Analysis of Binary Events.

Anup Amatya1, Dulal K Bhaumik, Sharon-Lise Normand

  • 1a Department of Health Sciences , New Mexico State University , Las Cruces , New Mexico , USA.

Journal of Biopharmaceutical Statistics
|June 12, 2014
PubMed
Summary
This summary is machine-generated.

Moment-based meta-analysis methods may be inaccurate, especially for rare binary outcomes. Likelihood-based mixed-effects models offer a more accurate alternative for evaluating medical intervention efficacy and safety.

Keywords:
HeterogeneityLogistic regressionRandom-effectRare event

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Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Evidence Synthesis

Background:

  • Meta-analysis is crucial for assessing medical intervention efficacy and safety.
  • Traditional moment-based meta-analytic methods face accuracy challenges, particularly with rare binary outcomes and heterogeneous effect sizes.

Purpose of the Study:

  • To compare likelihood-based mixed-effects modeling strategies against traditional moment-based methods in meta-analysis.
  • To evaluate the performance of different mixed-effects models for binary outcomes, focusing on estimation and testing of overall effect and heterogeneity.

Main Methods:

  • Comparative analysis of various mixed-effects modeling strategies.
  • Evaluation of model performance using metrics such as bias, type I error rate, and type II error rate.
  • Focus on meta-analysis of studies with binary outcomes.

Main Results:

  • Models allowing heterogeneity in both baseline rate and treatment effect demonstrated superior performance.
  • These advanced models exhibited lower type I and type II error rates.
  • Estimates derived from these models were found to be the least biased among those considered.

Conclusions:

  • Likelihood-based mixed-effects modeling, particularly models accommodating heterogeneity in baseline rates and treatment effects, provides a more accurate and reliable approach for meta-analysis.
  • These methods offer improved estimation and testing for overall effects and heterogeneity, especially for binary outcomes.