Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software

In generalized linear models, regression diagnostics including leverage, DFBETA and Cook's distance are commonly used to assess the influence of observations on the fit of a model. We illustrate how familiarity with the construction of common regression diagnostics formulae can lead to useful alternative formulae when the computer software of interest provides numerical values for only some of the component statistics. In particular, SAS software version 8.2 offers these diagnostics for logistic regression through PROC LOGISTIC, however PROC GENMOD does not compute them, so that, aside from residuals, diagnostics are not directly available from SAS for many generalized linear models. This article describes how these diagnostics may be obtained indirectly with alternative computational formulae based upon observation statistics that are produced as output by PROC GENMOD. Data from the Guidelines for Urinary Incontinence Discussion and Evaluation study, a randomized controlled trial directed at assessing the impact of urinary incontinence guideline adoption by primary care providers on patient outcomes, is used to illustrate the alternative computations.