可能性
发表于 2025-3-23 13:36:37
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公式
发表于 2025-3-23 14:45:23
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期满
发表于 2025-3-23 18:32:53
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.
编辑才信任
发表于 2025-3-23 22:51:03
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健壮
发表于 2025-3-24 03:34:34
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
强所
发表于 2025-3-24 09:09:52
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注意到
发表于 2025-3-24 10:41:53
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强制性
发表于 2025-3-24 15:18:17
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多余
发表于 2025-3-24 19:38:58
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.
拍翅
发表于 2025-3-25 00:19:19
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.