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

A. Lawrence Gould Mark Ernest Boye Michael J. Crowther Joseph G. Ibrahim George Quartey Sandrine Micallef Frédéric Y. Bois 1, 2
1 INERIS - Institut National de l'Environnement Industriel et des Risques
2 UTC - Université de Technologie de Compiègne
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.
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Submitted on : Tuesday, August 7, 2018 - 1:39:39 PM
Last modification on : Wednesday, August 8, 2018 - 1:00:49 AM

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A. Lawrence Gould, Mark Ernest Boye, Michael J. Crowther, Joseph G. Ibrahim, George Quartey, et al.. Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group. Statistics in Medicine, Wiley-Blackwell, 2015, 34 (14), pp.2181-2195. ⟨10.1002/sim.6141⟩. ⟨ineris-01855018⟩

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