Non-parametric frailty Cox models for hierarchical time-to-event data.

ABSTRACT: We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation-Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in si

SUBMITTER: Gasperoni F 

PROVIDER: S-EPMC6451633 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature