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Hierarchical regression for epidemiologic analyses of multiple exposures.
Many epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analyzed either by fitting a risk-regression model with all exposures forced in the model, or by using a preliminary-testing algorithm, such as stepwise regression, to produce a small...
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Formato: | Texto |
Lenguaje: | English |
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1994
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1566551/ https://www.ncbi.nlm.nih.gov/pubmed/7851328 |
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author | Greenland, S |
author_facet | Greenland, S |
author_sort | Greenland, S |
collection | PubMed |
description | Many epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analyzed either by fitting a risk-regression model with all exposures forced in the model, or by using a preliminary-testing algorithm, such as stepwise regression, to produce a smaller model. Research indicates that hierarchical modeling methods can outperform these conventional approaches. These methods are reviewed and compared to two hierarchical methods, empirical-Bayes regression and a variant here called "semi-Bayes" regression, to full-model maximum likelihood and to model reduction by preliminary testing. The performance of the methods in a problem of predicting neonatal-mortality rates are compared. Based on the literature to date, it is suggested that hierarchical methods should become part of the standard approaches to multiple-exposure studies. |
format | Text |
id | pubmed-1566551 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 1994 |
record_format | MEDLINE/PubMed |
spelling | pubmed-15665512006-09-19 Hierarchical regression for epidemiologic analyses of multiple exposures. Greenland, S Environ Health Perspect Research Article Many epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analyzed either by fitting a risk-regression model with all exposures forced in the model, or by using a preliminary-testing algorithm, such as stepwise regression, to produce a smaller model. Research indicates that hierarchical modeling methods can outperform these conventional approaches. These methods are reviewed and compared to two hierarchical methods, empirical-Bayes regression and a variant here called "semi-Bayes" regression, to full-model maximum likelihood and to model reduction by preliminary testing. The performance of the methods in a problem of predicting neonatal-mortality rates are compared. Based on the literature to date, it is suggested that hierarchical methods should become part of the standard approaches to multiple-exposure studies. 1994-11 /pmc/articles/PMC1566551/ /pubmed/7851328 Text en |
spellingShingle | Research Article Greenland, S Hierarchical regression for epidemiologic analyses of multiple exposures. |
title | Hierarchical regression for epidemiologic analyses of multiple exposures. |
title_full | Hierarchical regression for epidemiologic analyses of multiple exposures. |
title_fullStr | Hierarchical regression for epidemiologic analyses of multiple exposures. |
title_full_unstemmed | Hierarchical regression for epidemiologic analyses of multiple exposures. |
title_short | Hierarchical regression for epidemiologic analyses of multiple exposures. |
title_sort | hierarchical regression for epidemiologic analyses of multiple exposures. |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1566551/ https://www.ncbi.nlm.nih.gov/pubmed/7851328 |
work_keys_str_mv | AT greenlands hierarchicalregressionforepidemiologicanalysesofmultipleexposures |