Non-parametric frailty Cox models for hierarchical time-to-event data.
Authors
Ieva, Francesca
Paganoni, Anna Maria
Sharples, Linda
Publication Date
2020-07-01Journal Title
Biostatistics
ISSN
1465-4644
Publisher
Oxford University Press (OUP)
Volume
21
Issue
3
Pages
531-544
Language
eng
Type
Article
This Version
AM
Physical Medium
Print
Metadata
Show full item recordCitation
Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C., & Sharples, L. (2020). Non-parametric frailty Cox models for hierarchical time-to-event data.. Biostatistics, 21 (3), 531-544. https://doi.org/10.1093/biostatistics/kxy071
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 simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.
Keywords
Humans, Patient Admission, Cluster Analysis, Proportional Hazards Models, Statistical Distributions, Statistics, Nonparametric, Algorithms, Time Factors, Computer Simulation, Health Personnel, Time-to-Treatment
Sponsorship
MRC
Funder references
MRC (unknown)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1093/biostatistics/kxy071
This record's URL: https://www.repository.cam.ac.uk/handle/1810/287595
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