Purposeful selection of variables in logistic regression

In this paper we introduce an algorithm which automates that 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.We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data.If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.The criteria for inclusion of a variable in the model vary between problems and disciplines. The common approach to statistical model building is minimization of variables until the most parsimonious model that describes the data is found which also results in numerical stability and generalizability of the results. Some methodologists suggest inclusion of all clinical and other relevant variables in the model regardless of their significance in order to control for confounding. This approach, however, can lead to numerically unstable estimates and large standard errors. This paper is based on the purposeful selection of variables in regression methods (with specific focus on logistic regression in this paper) as proposed by Hosmer and Lemeshow [1,2].It is important to mention that with the rapid computing and information evolution there has been a growth in the field of feature selection methods and algorithms. Some examples include hill-climbing, greedy algorithms, recursive feature elimination, univariate association filtering, and backward/forward wrapping, to name a few. These methods have been used in