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Modified regression coefficient analysis for repeated binary measurements

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  • Chul Ahn
  • Sin-Ho Jung
  • Seung-Ho Kang

Abstract

Myers & Broyles (2000a, 2000b) illustrate that regression coefficient analysis (RCA) is a viable alternative to a generalized estimating equation (GEE) in the analysis of correlated binomial data. Since the regression coefficients (b i ' s ) may have different precisions, we modify RCA by weighting b i ' s by the inverses of their variances for statistical optimality. We perform the simulation study to evaluate the performance of RCA, modified RCA and GEE in terms of empirical type I errors and empirical powers of the regression coefficients in repeated binary measurement designs with and without dropouts. Two thousand data sets are generated using autoregressive (AR(1)) and compound symmetry (CS) correlation structures. We compare the type I errors and powers of RCA, modified RCA and GEE for the analysis of repeated binary measurement data as affected by different dropout mechanisms such as random dropouts and treatment dependent dropouts.

Suggested Citation

  • Chul Ahn & Sin-Ho Jung & Seung-Ho Kang, 2002. "Modified regression coefficient analysis for repeated binary measurements," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(5), pages 703-710.
    • Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:703-710
    DOI: 10.1080/02664760120098766
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    References listed on IDEAS

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    1. Leann Myers & Stephanie Broyles, 2000. "Regression coefficient analysis for correlated binomial outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(2), pages 217-234.
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    1. Seung-Ho Kang & Chul Ahn, 2001. "Regression coefficient analysis for correlated binomial outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 513-514.

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