Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited

Authors: Hsieh, Fushing¹; Tseng, Yi-Kuan²; Wang, Jane-Ling¹

Source: Biometrics, Volume 62, Number 4, 1 December 2006 , pp. 1037-1043(7)

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Abstract:

<sc>Summary</sc> The maximum likelihood approach to jointly model the survival time and its longitudinal covariates has been successful to model both processes in longitudinal studies. Random effects in the longitudinal process are often used to model the survival times through a proportional hazards model, and this invokes an EM algorithm to search for the maximum likelihood estimates (MLEs). Several intriguing issues are examined here, including the robustness of the MLEs against departure from the normal random effects assumption, and difficulties with the profile likelihood approach to provide reliable estimates for the standard error of the MLEs. We provide insights into the robustness property and suggest to overcome the difficulty of reliable estimates for the standard errors by using bootstrap procedures. Numerical studies and data analysis illustrate our points.

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1541-0420.2006.00570.x

Affiliations: 1: Department of Statistics, University of California, Davis, California 95616, U.S.A. 2: Graduate Institute of Statistics, National Central University, Jhongli City, Taoyuan County 32001, Taiwan

Publication date: 2006-12-01