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.


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@article{Witte1996SimulationSO, title={Simulation study of hierarchical regression.}, author={John S. Witte and Sander Greenland}, journal={Statistics in medicine}, year={1996}, volume={15 11}, pages={1161-70} }