SIMULATION STUDY OF HIERARCHICAL REGRESSION

✍ Scribed by JOHN S. WITTE; SANDER GREENLAND

• Book ID: 102650288
• Publisher: John Wiley and Sons
• Year: 1996
• Tongue: English
• Weight: 697 KB
• Volume: 15
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19960615)15:11<1161::aid-sim221>3.0.co;2-7

✦ Synopsis

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.