Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data. (English) Zbl 1520.62209
Summary: In this work, we propose a new Bayesian spatial homogeneity pursuit method for survival data under the proportional hazards model to detect spatially clustered patterns in baseline hazard and regression coefficients. Specially, regression coefficients and baseline hazard are assumed to have spatial homogeneity pattern over space. To capture such homogeneity, we develop a geographically weighted Chinese restaurant process prior to simultaneously estimating coefficients and baseline hazards and their uncertainty measures. An efficient Markov chain Monte Carlo (MCMC) algorithm is designed for our proposed methods. Performance is evaluated using simulated data, and further applied to a real data analysis of respiratory cancer in the state of Louisiana.
{© 2021 The International Biometric Society.}
{© 2021 The International Biometric Society.}
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
Keywords:
geographically weighted Chinese restaurant process; MCMC; piecewise constant baseline hazard; spatial clusteringReferences:
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