Titlebook: Survival Analysis with Correlated Endpoints; Joint Frailty-Copula Takeshi Emura,Shigeyuki Matsui,Virginie Rondeau Book 2019 The Author(s),

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Introduction to Multivariate Survival Analysis, likelihood-based method. Finally, we introduce two major procedures for describing dependence among event times: (i) the shared frailty models for clustered survival data (ii) the copula models for bivariate survival data. We provide some exercises at the end of this chapter.

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Setting the Scene,ew statistical issues in the analysis of survival data involving correlated endpoints and censoring. We then illustrate our motivations of investigating the interrelationship between endpoints using joint/bivariate survival models. We finally illustrate how copulas and bivariate survival models have

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Personalized Dynamic Prediction of Survival,a follow-up visit after surgery). This chapter considers dynamic prediction formulas of predicting survival for a cancer patient. The prediction formulas incorporate the genetic and clinical covariates collected on the patient entry as well as the tumour progression history evolving after the entry.

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The Joint Frailty-Copula Model for Correlated Endpoints,line-based models for baseline hazard functions with the aid of a penalized likelihood procedure. We analyze the data on ovarian cancer patients to illustrate statistical analyses using the . R package.