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Functional methods for logistic regression on random-effect-coefficients for longitudinal measurements

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  • Wang, C. Y.
  • Huang, Yijian

Abstract

We study logistic regression analysis when covariate variables are the underlying regression coefficients of another random effects model. For each subject, the covariate variables to the primary regression model are not observed, but can be estimated from observed longitudinal measurements. Wang et al. (Biometrics 56 (2000) 487-495) investigated estimation methods based on the regression calibration approximation and the expected estimating equations conditional on observed data. In this paper, we extend the sufficiency score and conditional score estimators of Stefanski and Carroll (Biometrics 74 (1987) 703-716) to this problem. These methods do not need the assumption of the underlying distribution of the random effects for each subject. We apply a robust sandwich covariance estimation procedure for both methods. Simulation results are provided for various random effects distributions.

Suggested Citation

  • Wang, C. Y. & Huang, Yijian, 2001. "Functional methods for logistic regression on random-effect-coefficients for longitudinal measurements," Statistics & Probability Letters, Elsevier, vol. 53(4), pages 347-356, July.
    • Handle: RePEc:eee:stapro:v:53:y:2001:i:4:p:347-356
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    1. C. Y. Wang & Naisyin Wang & Suojin Wang, 2000. "Regression Analysis When Covariates Are Regression Parameters of a Random Effects Model for Observed Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 56(2), pages 487-495, June.
    2. C.‐Y. Wang & Margaret Sullivan Pepe, 2000. "Expected estimating equations to accommodate covariate measurement error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 509-524.
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    1. Erning Li & Naisyin Wang & Nae-Yuh Wang, 2007. "Joint Models for a Primary Endpoint and Multiple Longitudinal Covariate Processes," Biometrics, The International Biometric Society, vol. 63(4), pages 1068-1078, December.
    2. Li, Erning & Zhang, Daowen & Davidian, Marie, 2007. "Likelihood and pseudo-likelihood methods for semiparametric joint models for a primary endpoint and longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5776-5790, August.

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