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Model Screening: How to Choose the Best Fitting Regression Model?
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Model Screening: How to Choose the Best Fitting Regression Model?
Stefan W. Roeder1  , Matthias Richter1 and Olf Herbarth1, 2
| (1) |
Department Human Exposure Research/Epidemiology, UFZ – Centre for Environmental Research Leipzig-Halle Ltd., D - 04318 Leipzig, Germany |
| (2) |
Faculty of Medicine, University of Leipzig, D - 04103 Leipzig, Germany |
Abstract
The problem space in epidemiological research is characterized by large datasets with many variables as candidates for logistic
regression model building. Out of these variables the variable combinations which form a sufficient logistic regression model
have to be selected. Usually methods like stepwise logistic regres‘sion apply.
These methods deliver suboptimal results in most cases, because they cannot screen the entire problem space which is formed
by different variable combinations with their resulting case set. Screening the entire problem space causes an enormous effort
in computing power. Furthermore the resulting models have to be judged. This paper describes an approach for calculating the
complete problem space using a computer grid as well as quality indicators for judgement of every particular model in order
to find the best fitting models.
We are using this system for epidemiological studies addressing specific problems in human epidemiology.
Keywords model screening - logistic regression - computer grid
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