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Regression coefficient analysis for correlated binomial outcomes

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  • Leann Myers
  • Stephanie Broyles

Abstract

At present, the generalized estimating equation (GEE) and weighted least-squares (WLS) regression methods are the most widely used methods for analyzing correlated binomial data; both are easily implemented using existing software packages. We propose an alternative technique, i.e. regression coefficient analysis (RCA), for this type of data. In RCA, a regression equation is computed for each of n individuals; regression coefficients are averaged across the n equations to produce a regression equation, which predicts marginal probabilities and which can be tested to address hypotheses of different slopes between groups, slopes different from zero, different intercepts, etc. The method is computationally simple and can be performed using standard software. Simulations and examples are used to compare the power and robustness of RCA with those of the standard GEE and WLS methods. We find that RCA is comparable with the GEE method under the conditions tested, and suggest that RCA, within specified limitations, is a viable alternative to the GEE and WLS methods in the analysis of correlated binomial data.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:217-234
    DOI: 10.1080/02664760021754
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    Cited by:

    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.
    2. 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.

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