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Regression analysis of longitudinal data with correlated censoring and observation times

Author

Listed:
  • Yang Li

    (University of North Carolina at Charlotte)

  • Xin He

    (University of Maryland)

  • Haiying Wang

    (University of New Hampshire)

  • Jianguo Sun

    (University of Missouri)

Abstract

Longitudinal data occur in many fields such as the medical follow-up studies that involve repeated measurements. For their analysis, most existing approaches assume that the observation or follow-up times are independent of the response process either completely or given some covariates. In practice, it is apparent that this may not be true. In this paper, we present a joint analysis approach that allows the possible mutual correlations that can be characterized by time-dependent random effects. Estimating equations are developed for the parameter estimation and the resulted estimators are shown to be consistent and asymptotically normal. The finite sample performance of the proposed estimators is assessed through a simulation study and an illustrative example from a skin cancer study is provided.

Suggested Citation

  • Yang Li & Xin He & Haiying Wang & Jianguo Sun, 2016. "Regression analysis of longitudinal data with correlated censoring and observation times," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 343-362, July.
    • Handle: RePEc:spr:lifeda:v:22:y:2016:i:3:d:10.1007_s10985-015-9334-z
    DOI: 10.1007/s10985-015-9334-z
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    References listed on IDEAS

    as
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