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- W3048449902 abstract "Survival data that have a multivariate structure occur in many health sciences including biomedical research, epidemiology studies and, clinical trials. In most cases, an analysis of multivariate survival data deals with association structures among survival times within same subjects or clusters. Under the conditional (frailty) model approach, the baseline survival functions are modified using mixed effects that incorporates cluster-specific random effects. This approach can be routinely applied and implemented in several statistical software packages with tools to handle analyses of clustered survival data. The random cluster terms are typically assumed to be independently and identically distributed from a known parametric distribution. However, in most practical application, the random effects may change over time, and the assumed parametric random effect distribution could be incorrect. In such cases, nonparametric forms could be used instead. In this chapter, we develop and apply two approaches that assume (a) time dependent random effects and (b) nonparametric random effect distribution. For both approaches, full Bayesian inference using the Gibbs sampler algorithm is used for computing posterior parameter for the mixing distribution and regression coefficients. The proposed methodological approaches are demonstrated using real data sets." @default.
- W3048449902 created "2020-08-18" @default.
- W3048449902 creator A5072464330 @default.
- W3048449902 date "2020-01-01" @default.
- W3048449902 modified "2023-10-14" @default.
- W3048449902 title "Flexible Modeling of Frailty Effects in Clustered Survival Data" @default.
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- W3048449902 doi "https://doi.org/10.1007/978-3-030-42196-0_21" @default.
- W3048449902 hasPublicationYear "2020" @default.
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