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A profile likelihood approach for longitudinal data analysis

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  • Ziqi Chen
  • Man†Lai Tang
  • Wei Gao

Abstract

Inappropriate choice of working correlation structure in generalized estimating equations (GEE) could lead to inefficient parameter estimation while impractical normality assumption in likelihood approach would limit its applicability in longitudinal data analysis. In this article, we propose a profile likelihood method for estimating parameters in longitudinal data analysis via maximizing the estimated likelihood. The proposed method yields consistent and efficient estimates without specifications of the working correlation structure nor the underlying error distribution. Both theoretical and simulation results confirm the satisfactory performance of the proposed method. We illustrate our methodology with a diastolic blood pressure data set.

Suggested Citation

  • Ziqi Chen & Man†Lai Tang & Wei Gao, 2018. "A profile likelihood approach for longitudinal data analysis," Biometrics, The International Biometric Society, vol. 74(1), pages 220-228, March.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:1:p:220-228
    DOI: 10.1111/biom.12712
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    References listed on IDEAS

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    6. Ziqi Chen & Man-Lai Tang & Wei Gao & Ning-Zhong Shi, 2014. "New Robust Variable Selection Methods for Linear Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 725-741, September.
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    Cited by:

    1. Yayuan Zhu & Ziqi Chen & Jerald F. Lawless, 2022. "Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 236-264, March.

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