ArticleThe annals of applied statistics2015
MODELLING COUNTY LEVEL BREAST CANCER SURVIVAL DATA USING A COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL.
Article in The annals of applied statistics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Geospatial Variation and Determinants of Time to Pregnancy Loss Among Reproductive-Aged Women in East Africa: Bayesian Spatial Frailty Model.BioMed research international · 2026Article
- A Multilevel Spatial Survival Analysis of Patients in Texas with End-Stage Renal Disease.Healthcare (Basel, Switzerland) · 2025Article
- Tweedie Compound Poisson Models with Covariate-Dependent Random Effects for Multilevel Semicontinuous Data.Entropy (Basel, Switzerland) · 2023Article
- Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality.Computational and mathematical methods in medicine · 2020Article
- Generalized accelerated failure time spatial frailty model for arbitrarily censored data.Lifetime data analysis · 2017Article
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Authors and funding
4 authors.
Funding
Abstract
Understanding the factors that explain differences in survival times is an important issue for establishing policies to improve national health systems. Motivated by breast cancer data arising from the Surveillance Epidemiology and End Results program, we propose a covariate-adjusted proportional hazards frailty model for the analysis of clustered right-censored data. Rather than incorporating exchangeable frailties in the linear predictor of commonly-used survival models, we allow the frailty distribution to flexibly change with both continuous and categorical cluster-level covariates and model them using a dependent Bayesian nonparametric model. The resulting process is flexible and easy to fit using an existing R package. The application of the model to our motivating example showed that, contrary to intuition, those diagnosed during a period of time in the 1990s in more rural and less affluent Iowan counties survived breast cancer better. Additional analyses showed the opposite trend for earlier time windows. We conjecture that this anomaly has to be due to increased hormone replacement therapy treatments prescribed to more urban and affluent subpopulations.
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