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Sequential adaptive variables and subject selection for GEE methods

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  • Zimu Chen
  • Zhanfeng Wang
  • Yuan‐chin Ivan Chang

Abstract

Modeling correlated or highly stratified multiple‐response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time‐consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active‐learning methods found in the machine‐learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.

Suggested Citation

  • Zimu Chen & Zhanfeng Wang & Yuan‐chin Ivan Chang, 2020. "Sequential adaptive variables and subject selection for GEE methods," Biometrics, The International Biometric Society, vol. 76(2), pages 496-507, June.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:2:p:496-507
    DOI: 10.1111/biom.13160
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    References listed on IDEAS

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    1. Zhanfeng Wang & Yuan-chin Chang, 2013. "Sequential estimate for linear regression models with uncertain number of effective variables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(7), pages 949-978, October.
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    Cited by:

    1. Bo-Shiang Ke & Yuan-chin Ivan Chang, 2021. "A Model-Free Subject Selection Method for Active Learning Classification Procedures," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 544-555, October.

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