Simulation study of hierarchical regression.
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.
Witte
J S
JS
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44109-1998, USA.
Greenland
S
S
D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000178:diet therapy; D016022:Case-Control Studies; D016001:Confidence Intervals; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design