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PubMed · 8804145

Simulation study of hierarchical regression.

J S Witte·S Greenland
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Abstract

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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Bayes TheoremBreast NeoplasmsCase-Control StudiesConfidence IntervalsFemaleHumansLeast-Squares AnalysisLikelihood FunctionsModels, StatisticalRegression AnalysisResearch DesignComparative StudyJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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J S Witte, S Greenland. 1996-06-15. Simulation study of hierarchical regression.. https://doi.org/10.1002/(sici)1097-0258(19960615)15%3A11%3C1161%3A%3Aaid-sim221%3E3.0.co%3B2-7

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