Mixed Effects Logistic Regression Models for Longitudinal Ordinal Functional Response Data with Multiple-Cause Drop-Out from the Longitudinal Study of Aging
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- P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
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- Yu, Lei & Tyas, Suzanne L. & Snowdon, David A. & Kryscio, Richard J., 2009. "Effects of ignoring baseline on modeling transitions from intact cognition to dementia," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3334-3343, July.
- Walter Dempsey & Peter McCullagh, 2018. "Survival models and health sequences," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 550-584, October.
- 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.
- Garrett M. Fitzmaurice & Stuart R. Lipsitz & Geert Molenberghs & Joseph G. Ibrahim, 2005. "A protective estimator for longitudinal binary data subject to non‐ignorable non‐monotone missingness," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 723-735, November.
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