Purposeful selection of variables in logistic regression

The main problem in any model-building situation is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms embedded in SAS PROC LOGISTIC. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow [2000] describe a purposeful selection of covariates algorithm within which an analyst makes a variable selection decision at each step of the modeling process. In this paper we introduce a macro, %PurposefulSelection, which automates this process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results and implications are discussed in more detail.

• Type: Journal
• Year: 2008
• Where: SCFBM
• Authors: Zoran Bursac, C. Heath Gauss, David Keith Williams, David W. Hosmer
• Keywords: Algorithm, SCFBM 2008, Variable Selection, Variable Selection Decision