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Overdispersion models for correlated multinomial data: Applications to blinding assessment.

V Landsman1,2, D Landsman3, C S Li4

  • 1Institute for Work and Health, Toronto, Ontario, Canada.

Statistics in Medicine
|August 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a multinomial overdispersion model for correlated data, estimating a Global Blinding Index using generalized estimating equations (GEE). GEE estimators performed well, even with few clusters, outperforming other methods in clinical trials and meta-analyses.

Keywords:
Dirichlet-multinomialGEEblinding indexmeta-analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Correlated Data Analysis

Background:

  • Overdispersion models are well-studied for normal and binomial data but less so for multinomial data.
  • Correlated multinomial data present unique analytical challenges in clinical trials and meta-analyses.

Purpose of the Study:

  • To develop and evaluate a multinomial overdispersion model for correlated data.
  • To introduce and estimate a Global Blinding Index using generalized estimating equations (GEE).
  • To assess the performance of GEE estimators under various conditions, including small cluster numbers and high intraclass correlation.

Main Methods:

  • Specification of a multinomial overdispersion model defining the first two moments of the outcome.
  • Application of generalized estimating equations (GEE) for parameter estimation.
  • Simulation studies and comparisons with inverse-variance weighted and maximum-likelihood estimators.

Main Results:

  • GEE estimators for the Global Blinding Index demonstrated satisfactory performance, even with a small number of clusters.
  • Inverse-variance weighted estimators performed poorly, particularly with high intraclass correlation coefficients.
  • The proposed GEE method proved robust across different simulation scenarios.

Conclusions:

  • The developed multinomial overdispersion model and GEE approach are effective for analyzing clustered multinomial data.
  • The GEE method offers a reliable alternative to other estimators, especially in challenging data situations.
  • Findings provide practical guidance for analyzing clustered multinomial data in clinical trials and meta-analyses.