THE PERILS OF STEPWISE LOGISTIC REGRESSION AND HOW TO ESCAPE THEM USING INFORMATION CRITERIA AND THE OUTPUT DELIVERY SYSTEM

In this presentation, which is a continuation of our NESUG’2000 paper, we demonstrate that using SAS® stepwise logistic regression with the default and most typically used value of significance level for entry (SLENTRY) of 0.05 may be unreasonable and sometimes even dangerous because it results in the model that on one hand has usually too many variables for a reliable interpretation and on the other hand too few variables for a good prediction. Users who blindly rely on stepwise logistic regression will most likely get a rather poor choice for both purposes: interpretation and prediction. The recommendations of using critical p-values other than the default often look vague and even contradictory. We propose to resolve this problem by using the Akaike and Schwarz information criteria (which are standard components of the PROC LOGISTIC output), some elements of Bayesian reasoning, and capabilities of ODS (Output Delivery System) which are available in PROC LOGISTIC in SAS version 8. We also discuss the problem of improving the model selection process by taking into account model selection uncertainty. The intended audience: SAS users of all levels who work with SAS/STAT® and PROC LOGISTIC in particular. THE PROBLEMS WITH MODEL SELECTION Model selection is a fundamental task in data analysis, widely recognized as central to good inference. In SAS PROC LOGISTIC, we have 4 automatic model selection techniques: forward selection, backward elimination, stepwise selection which combines the elements of the previous two, and the best subset selection procedure. The first three methods are based on the same ideas and we will talk only about stepwise selection as more flexible and sophisticated selection procedure. This choice is subjective, some researchers prefer to work with backward selection. Typically, the final model selected by each of these procedures will be the same, but it is in no way guaranteed. Stepwise selection is intuitively appealing: it builds models in a sequential manner and it allows for the examination of a collection of models which might not otherwise have been examined. The best subsets selection method which is invoked with the statement SELECTION = SCORE is not as popular as forward, backward, and stepwise selections because it can compare only the models of the same size (with the same number of covariates). However, we will show how the best subset selection method can be very useful in the final step of our procedure in reducing model selection uncertainty. Purposeful selection which combines subject measure knowledge with statistical significance considerations can be performed only when we have a small number of models to compare originally, or at some advanced step of selection when a small number of covariates has been left. It is worth noting that if we have 10 covariates , the number of all possible models is 2 =1024. With 20 covariates we have more than 1,000, 000 possible models, and with 30 covariates the number of possible models is greater than 1,000,000,000. Thus, even with rather moderate numbers of covariates we cannot do without stepwise selection. The stepwise technique allows us to decrease drastically the total number of models under consideration and to produce the final model. The final result will depend substantially on the 2 parameters: SLENTRY (the significance level for entering) and SLSTAY (the significance level for stay). If the values of these parameters are not specified, the SAS system uses default values of 0.05 for both. This default value of the significance level is used more often than not without any grounds, just because of an unwritten statistical tradition which says: if you do not have strong personal opinions on this matter, then use 0.05. SLENTRY=0.05 does not mean that the overall significance level is 0.05, it is usually much larger than 5%. One way to deal with this problem is to specify a very small SLENTRY (see, for example, Posters