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Performance of models for estimating absolute risk difference in multicenter trials with binary outcome.

Claudia Pedroza1, Van Thi Truong2

  • 1Center for Clinical Research and Evidence-Based Medicine, McGovern Medical School, 6431 Fannin St., MSB 2.106, Houston, 77030, TX, USA. claudia.pedroza@uth.tmc.edu.

BMC Medical Research Methodology
|September 1, 2016
PubMed
Summary

For estimating absolute risk difference (RD) in multicenter studies, binomial or Poisson generalized estimating equation (GEE) models with an identity link are recommended. These models offer reliable performance for correlated binary outcomes.

Keywords:
Clustered dataCorrelated binary dataGeneralized estimating equationMulticenter trialRisk differenceRobust standard errors

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research

Background:

  • Reporting absolute risk difference (RD) is crucial for prospective clinical and epidemiological studies.
  • Multicenter study analyses often require adjustment for center effects due to outcome variations or stratified randomization.
  • Regression methods are used for adjusted RD estimation, but their performance lacks formal evaluation.

Purpose of the Study:

  • To formally evaluate the performance of six regression methods for estimating absolute risk difference (RD) in multicenter studies.
  • To compare regression-based RD estimates with unadjusted estimates under various simulation scenarios.
  • To identify optimal regression models for analyzing correlated binary outcomes in prospective studies.

Main Methods:

  • A simulation study was conducted to assess six regression models within a generalized estimating equation (GEE) framework.
  • Models evaluated included binomial, Poisson, Normal, log binomial, log Poisson, and logistic regression with identity or log links.
  • Simulations varied factors like response function, subject numbers, risk difference, outcome rates, predictors, and intracenter correlation.

Main Results:

  • Most models performed similarly, but the log binomial model frequently failed to converge when including a baseline predictor.
  • Binomial and Poisson identity models showed best performance near outcome rate boundaries, though differences were minor.
  • Unadjusted methods introduced minimal bias but sometimes had inflated or deficient confidence interval coverage for RD.

Conclusions:

  • Binomial or Poisson GEE models with an identity link are recommended for estimating RD with correlated binary data.
  • Logistic regression, log Poisson regression, or linear regression GEE models serve as viable alternatives if primary recommendations fail.
  • These findings guide the selection of appropriate statistical methods for analyzing risk differences in complex study designs.