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Joint Models for Longitudinal and Survival Data

  • Joseph G. Ibrahim
  • Ming-Hui Chen
  • Debajyoti Sinha
Part of the Springer Series in Statistics book series (SSS)

Abstract

Joint models for survival and longitudinal data have recently become quite popular in cancer and AIDS clinical trials, where a longitudinal biologic marker such as CD4 count or immune response can be an important predictor of survival. Often in clinical trials where the primary endpoint is time to an event, patients are also monitored longitudinally with respect to one or more biologic endpoints throughout the follow-up period. This may be done by taking immunologic or virologic measures in the case of infectious diseases or perhaps with a questionnaire assessing the quality of life after receiving a particular treatment. Often these longitudinal measures are incomplete or may be prone to measurement error. These measurements are also important because they may be predictive of survival. Therefore methods which can model both the longitudinal and the survival components jointly are becoming increasingly essential in most cancer and AIDS clinical trials.

Keywords

Joint Modeling High Posterior Density Partial Likelihood Conditional Posterior Distribution Trajectory Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Joseph G. Ibrahim
    • 1
  • Ming-Hui Chen
    • 2
  • Debajyoti Sinha
    • 3
  1. 1.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA
  2. 2.Department of Mathematical SciencesWorcester Polytechnic InstituteWorcesterUSA
  3. 3.Department of Biometry and EpidemiologyMedical Universtiy of South CarolinaCharlestonUSA

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