Likelihood and pseudo-likelihood methods for semiparametric joint models for a primary endpoint and longitudinal data
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- 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.
- Erning Li & Daowen Zhang & Marie Davidian, 2004. "Conditional Estimation for Generalized Linear Models When Covariates Are Subject-Specific Parameters in a Mixed Model for Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 60(1), pages 1-7, March.
- Daowen Zhang & Marie Davidian, 2001. "Linear Mixed Models with Flexible Distributions of Random Effects for Longitudinal Data," Biometrics, The International Biometric Society, vol. 57(3), pages 795-802, September.
- Xiao Song & Marie Davidian & Anastasios A. Tsiatis, 2002. "A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 58(4), pages 742-753, December.
- 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.
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- Duchwan Ryu & Erning Li & Bani K. Mallick, 2011. "Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 67(2), pages 454-466, June.
- Bernhardt, Paul W. & Zhang, Daowen & Wang, Huixia Judy, 2015. "A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 37-53.
- Li, Erning & Pourahmadi, Mohsen, 2013. "An alternative REML estimation of covariance matrices in linear mixed models," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1071-1077.
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