Semiparametric Bayesian analysis of high-dimensional censored outcome data
The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data for US individuals diagnosed with cancer. Semiparametric Bayesian methods are computationally expensive to fit for such large data-sets. This paper develops a cost-effective Markov chain Monte Carlo strategy...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-07-01
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| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2017.1396436 |
| _version_ | 1797677078921871360 |
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| author | Chetkar Jha Yi Li Subharup Guha |
| author_facet | Chetkar Jha Yi Li Subharup Guha |
| author_sort | Chetkar Jha |
| collection | DOAJ |
| description | The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data for US individuals diagnosed with cancer. Semiparametric Bayesian methods are computationally expensive to fit for such large data-sets. This paper develops a cost-effective Markov chain Monte Carlo strategy for censored outcomes to fit a semiparametric bayesian analysis of SEER data of New Mexico. We use an accelerated failure time model, with Dirichlet process random effects for inter-subject variation, and intrinsic conditionally autoregressive random effects for spatial correlations. The results offer insights into differences in breast cancer mortality rates between ethnic groups, tumor grade and spatial effect of counties. |
| first_indexed | 2024-03-11T22:39:50Z |
| format | Article |
| id | doaj.art-61637336ee4a4ccabaab836346d6e89e |
| institution | Directory Open Access Journal |
| issn | 2475-4269 2475-4277 |
| language | English |
| last_indexed | 2024-03-11T22:39:50Z |
| publishDate | 2017-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Statistical Theory and Related Fields |
| spelling | doaj.art-61637336ee4a4ccabaab836346d6e89e2023-09-22T09:19:44ZengTaylor & Francis GroupStatistical Theory and Related Fields2475-42692475-42772017-07-011219420410.1080/24754269.2017.13964361396436Semiparametric Bayesian analysis of high-dimensional censored outcome dataChetkar Jha0Yi Li1Subharup Guha2University of MissouriUniversity of MichiganUniversity of MissouriThe Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data for US individuals diagnosed with cancer. Semiparametric Bayesian methods are computationally expensive to fit for such large data-sets. This paper develops a cost-effective Markov chain Monte Carlo strategy for censored outcomes to fit a semiparametric bayesian analysis of SEER data of New Mexico. We use an accelerated failure time model, with Dirichlet process random effects for inter-subject variation, and intrinsic conditionally autoregressive random effects for spatial correlations. The results offer insights into differences in breast cancer mortality rates between ethnic groups, tumor grade and spatial effect of counties.http://dx.doi.org/10.1080/24754269.2017.1396436icar modelsdata squashingdirichlet processgeneralised pólya urn processbig data |
| spellingShingle | Chetkar Jha Yi Li Subharup Guha Semiparametric Bayesian analysis of high-dimensional censored outcome data Statistical Theory and Related Fields icar models data squashing dirichlet process generalised pólya urn process big data |
| title | Semiparametric Bayesian analysis of high-dimensional censored outcome data |
| title_full | Semiparametric Bayesian analysis of high-dimensional censored outcome data |
| title_fullStr | Semiparametric Bayesian analysis of high-dimensional censored outcome data |
| title_full_unstemmed | Semiparametric Bayesian analysis of high-dimensional censored outcome data |
| title_short | Semiparametric Bayesian analysis of high-dimensional censored outcome data |
| title_sort | semiparametric bayesian analysis of high dimensional censored outcome data |
| topic | icar models data squashing dirichlet process generalised pólya urn process big data |
| url | http://dx.doi.org/10.1080/24754269.2017.1396436 |
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