| Simulation study of hierarchical regression. | |
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MedLine Citation:
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PMID: 8804145 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Hierarchical regression - which attempts to improve standard regression estimates by adding a second-stage 'prior' regression to an ordinary model - provides a practical approach to evaluating multiple exposures. We present here a simulation study of logistic regression in which we compare hierarchical regression fitted by a two-stage procedure to ordinary maximum likelihood. The simulations were based on case-control data on diet and breast cancer, where the hierarchical model uses a second-stage regression to pull conventional dietary-item estimates toward each other when they have similar levels of food constituents. Our results indicate that hierarchical modelling of continuous covariates offers worthwhile improvement over ordinary maximum-likelihood, provided one does not underspecify the second-stage standard deviations. |
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Authors:
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J S Witte; S Greenland |
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Publication Detail:
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Type: Comparative Study; Journal Article; Research Support, U.S. Gov't, P.H.S. |
Journal Detail:
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Title: Statistics in medicine Volume: 15 ISSN: 0277-6715 ISO Abbreviation: Stat Med Publication Date: 1996 Jun |
Date Detail:
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Created Date: 1996-11-19 Completed Date: 1996-11-19 Revised Date: 2007-11-14 |
Medline Journal Info:
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Nlm Unique ID: 8215016 Medline TA: Stat Med Country: ENGLAND |
Other Details:
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Languages: eng Pagination: 1161-70 Citation Subset: IM |
Affiliation:
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Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44109-1998, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Bayes Theorem Breast Neoplasms / diet therapy Case-Control Studies Confidence Intervals Female Humans Least-Squares Analysis Likelihood Functions Models, Statistical* Regression Analysis* Research Design |
| Grant Support | |
ID/Acronym/Agency:
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CA-09142/CA/NCI NIH HHS; ES-07050/ES/NIEHS NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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