Journal
STATISTICS IN MEDICINE
Volume 34, Issue 14, Pages 2181-2195Publisher
WILEY
DOI: 10.1002/sim.6141
Keywords
time-dependent; random effects; software; applications
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Funding
- National Institute for Health Research [DRF-2012-05-409] Funding Source: researchfish
- National Institutes of Health Research (NIHR) [DRF-2012-05-409] Funding Source: National Institutes of Health Research (NIHR)
- NCI NIH HHS [P01 CA142538] Funding Source: Medline
- Department of Health [DRF-2012-05-409] Funding Source: Medline
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Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology. Copyright (c) 2014 John Wiley & Sons, Ltd.
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