Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer
The stereotype regression model for categorical outcomes, proposed by Anderson ( J. Roy. Statist. Soc. B. 1984; 46 :1–30) is nested between the baseline-category logits and adjacent category logits model with proportional odds structure. The stereotype model is more parsimonious than the ordinary ba...
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2009
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Online Access: | https://hdl.handle.net/2027.42/64310 http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19731262&dopt=citation https://doi.org/10.1002/sim.3693 |
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ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/64310 2023-08-20T04:06:59+02:00 Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer Ahn, Jaeil Mukherjee, Bhramar Banerjee, Mousumi Cooney, Kathleen A. Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A. Department of Internal Medicine and Urology, University of Michigan, Ann Arbor, MI 48109, U.S.A. University of Michigan Comprehensive Cancer Center, Ann Arbor, MI 48109, U.S.A. 2009-11-10 177227 bytes 3118 bytes application/pdf text/plain https://hdl.handle.net/2027.42/64310 http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19731262&dopt=citation https://doi.org/10.1002/sim.3693 unknown John Wiley & Sons, Ltd. Ahn, Jaeil; Mukherjee, Bhramar; Banerjee, Mousumi; Cooney, Kathleen A. (2009). "Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer." Statistics in Medicine 28(25): 3139-3157. <http://hdl.handle.net/2027.42/64310> 0277-6715 1097-0258 https://hdl.handle.net/2027.42/64310 http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19731262&dopt=citation 19731262 doi:10.1002/sim.3693 Statistics in Medicine IndexNoFollow Mathematics and Statistics Medicine (General) Statistics and Numeric Data Public Health Health Sciences Science Social Sciences Article 2009 ftumdeepblue https://doi.org/10.1002/sim.3693 2023-07-31T21:08:27Z The stereotype regression model for categorical outcomes, proposed by Anderson ( J. Roy. Statist. Soc. B. 1984; 46 :1–30) is nested between the baseline-category logits and adjacent category logits model with proportional odds structure. The stereotype model is more parsimonious than the ordinary baseline-category (or multinomial logistic) model due to a product representation of the log-odds-ratios in terms of a common parameter corresponding to each predictor and category-specific scores. The model could be used for both ordered and unordered outcomes. For ordered outcomes, the stereotype model allows more flexibility than the popular proportional odds model in capturing highly subjective ordinal scaling which does not result from categorization of a single latent variable, but are inherently multi-dimensional in nature. As pointed out by Greenland ( Statist. Med. 1994; 13 :1665–1677), an additional advantage of the stereotype model is that it provides unbiased and valid inference under outcome-stratified sampling as in case–control studies. In addition, for matched case–control studies, the stereotype model is amenable to classical conditional likelihood principle, whereas there is no reduction due to sufficiency under the proportional odds model. In spite of these attractive features, the model has been applied less, as there are issues with maximum likelihood estimation and likelihood-based testing approaches due to non-linearity and lack of identifiability of the parameters. We present comprehensive Bayesian inference and model comparison procedure for this class of models as an alternative to the classical frequentist approach. We illustrate our methodology by analyzing data from The Flint Men's Health Study, a case–control study of prostate cancer in African-American men aged 40–79 years. We use clinical staging of prostate cancer in terms of Tumors, Nodes and Metastasis as the categorical response of interest. Copyright © 2009 John Wiley & Sons, Ltd. Peer Reviewed ... Article in Journal/Newspaper Greenland University of Michigan: Deep Blue Greenland Flint ENVELOPE(-65.417,-65.417,-67.333,-67.333) Statistics in Medicine 28 25 3139 3157 |
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University of Michigan: Deep Blue |
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Mathematics and Statistics Medicine (General) Statistics and Numeric Data Public Health Health Sciences Science Social Sciences |
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Mathematics and Statistics Medicine (General) Statistics and Numeric Data Public Health Health Sciences Science Social Sciences Ahn, Jaeil Mukherjee, Bhramar Banerjee, Mousumi Cooney, Kathleen A. Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer |
topic_facet |
Mathematics and Statistics Medicine (General) Statistics and Numeric Data Public Health Health Sciences Science Social Sciences |
description |
The stereotype regression model for categorical outcomes, proposed by Anderson ( J. Roy. Statist. Soc. B. 1984; 46 :1–30) is nested between the baseline-category logits and adjacent category logits model with proportional odds structure. The stereotype model is more parsimonious than the ordinary baseline-category (or multinomial logistic) model due to a product representation of the log-odds-ratios in terms of a common parameter corresponding to each predictor and category-specific scores. The model could be used for both ordered and unordered outcomes. For ordered outcomes, the stereotype model allows more flexibility than the popular proportional odds model in capturing highly subjective ordinal scaling which does not result from categorization of a single latent variable, but are inherently multi-dimensional in nature. As pointed out by Greenland ( Statist. Med. 1994; 13 :1665–1677), an additional advantage of the stereotype model is that it provides unbiased and valid inference under outcome-stratified sampling as in case–control studies. In addition, for matched case–control studies, the stereotype model is amenable to classical conditional likelihood principle, whereas there is no reduction due to sufficiency under the proportional odds model. In spite of these attractive features, the model has been applied less, as there are issues with maximum likelihood estimation and likelihood-based testing approaches due to non-linearity and lack of identifiability of the parameters. We present comprehensive Bayesian inference and model comparison procedure for this class of models as an alternative to the classical frequentist approach. We illustrate our methodology by analyzing data from The Flint Men's Health Study, a case–control study of prostate cancer in African-American men aged 40–79 years. We use clinical staging of prostate cancer in terms of Tumors, Nodes and Metastasis as the categorical response of interest. Copyright © 2009 John Wiley & Sons, Ltd. Peer Reviewed ... |
author2 |
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A. Department of Internal Medicine and Urology, University of Michigan, Ann Arbor, MI 48109, U.S.A. University of Michigan Comprehensive Cancer Center, Ann Arbor, MI 48109, U.S.A. |
format |
Article in Journal/Newspaper |
author |
Ahn, Jaeil Mukherjee, Bhramar Banerjee, Mousumi Cooney, Kathleen A. |
author_facet |
Ahn, Jaeil Mukherjee, Bhramar Banerjee, Mousumi Cooney, Kathleen A. |
author_sort |
Ahn, Jaeil |
title |
Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer |
title_short |
Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer |
title_full |
Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer |
title_fullStr |
Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer |
title_full_unstemmed |
Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer |
title_sort |
bayesian inference for the stereotype regression model: application to a case–control study of prostate cancer |
publisher |
John Wiley & Sons, Ltd. |
publishDate |
2009 |
url |
https://hdl.handle.net/2027.42/64310 http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19731262&dopt=citation https://doi.org/10.1002/sim.3693 |
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Greenland Flint |
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Greenland |
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Ahn, Jaeil; Mukherjee, Bhramar; Banerjee, Mousumi; Cooney, Kathleen A. (2009). "Bayesian inference for the stereotype regression model: Application to a case–control study of prostate cancer." Statistics in Medicine 28(25): 3139-3157. <http://hdl.handle.net/2027.42/64310> 0277-6715 1097-0258 https://hdl.handle.net/2027.42/64310 http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19731262&dopt=citation 19731262 doi:10.1002/sim.3693 Statistics in Medicine |
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https://doi.org/10.1002/sim.3693 |
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Statistics in Medicine |
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