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Stat Med. 2015 Jun 30;34(14):2181-95. doi: 10.1002/sim.6141. Epub 2014 Mar 14.

Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group.

Author information

1
Merck Research Laboratories, 351 North Sumneytown Pike, North Wales, PA 19454, U.S.A.
2
Eli Lilly, 893 S. Delaware Street, Indianapolis, IN 46285, U.S.A.
3
Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, U.K.
4
Department of Statistics and Operations Research, University of North Carolina, 318 Hanes Hall Chapel Hill, NC 27599, U.S.A.
5
Genentech, South San Francisco, CA, U.S.A.
6
Sanofi-Aventis, Paris, France.
7
Université de Technologie de Compiègne, Centre de Recherche de Royallieu, 60205 Compiègne Cedex, France.
8
INERIS/CRD/VIVA/METO, Verneuil en Halatte, France.

Abstract

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.

KEYWORDS:

applications; random effects; software; time-dependent

PMID:
24634327
PMCID:
PMC4677775
DOI:
10.1002/sim.6141
[Indexed for MEDLINE]
Free PMC Article

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