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Regression analysis for differentially misclassified correlated binary outcomes

Author

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  • Li Tang
  • Robert H. Lyles
  • Caroline C. King
  • Joseph W. Hogan
  • Yungtai Lo

Abstract

type="main" xml:id="rssc12081-abs-0001"> In many epidemiological and clinical studies, misclassification may arise in one or several variables, resulting in potentially invalid analytic results (e.g. estimates of odds ratios of interest) when no correction is made. Here we consider the situation in which correlated binary response variables are subject to misclassification. Building on prior work, we provide an approach to adjust for potentially complex differential misclassification via internal validation sampling applied at multiple study time points. We seek to estimate the parameters of a primary generalized linear mixed model that accounts for baseline and/or time-dependent covariates. The misclassification process is modelled via a second generalized linear model that captures variations in sensitivity and specificity parameters according to time and a set of subject-specific covariates that may or may not overlap with those in the primary model. Simulation studies demonstrate the precision and validity of the method proposed. An application is presented based on longitudinal assessments of bacterial vaginosis conducted in the ‘HIV epidemiology research’ study.

Suggested Citation

  • Li Tang & Robert H. Lyles & Caroline C. King & Joseph W. Hogan & Yungtai Lo, 2015. "Regression analysis for differentially misclassified correlated binary outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(3), pages 433-449, April.
    • Handle: RePEc:bla:jorssc:v:64:y:2015:i:3:p:433-449
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    File URL: http://hdl.handle.net/10.1111/rssc.2015.64.issue-3
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    Cited by:

    1. Li Yige & VanderWeele Tyler J., 2020. "Direct Effects under Differential Misclassification in Outcomes, Exposures, and Mediators," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 286-299, January.

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