A selection algorithm would be a great feature to have in GENMOD. Although automatic selection methods are controversial in some instances, in some cases all one needs is a reasonable good-enough model with some of the noise removed. It would also be great to be able to obtain such model within a reasonable time and without too much programming. In absence of the repeated measures, you could conduct the analysis in R, using the step() function. This function finds a model that minimizes either AIC or BIC, using a backward, forward, or stepwise (both backward and forward) searches. The function should work with models of the following families:  binomial, gaussian, Gamma, inverse.gaussian,  poisson, quasibinomial, quasipoisson. The  quasibinomial and quasipoisson families are the over-dispersed versions of the binomial and poisson, respectively. However, the situation is even more complex when you have repeated measures. As far as I know there are no readily available selection algorithms for generalized linear models with repeated measures. A couple of months ago, I was working on a similar problem, and all I could find was a couple of experimental R packages, and that's about it. Aside from the traditional stats methodology, there are some convoluted ways to approach the problem using data mining techniques, for instance: assuming that all subjects have similar number and timing for the repeated measures, you could conduct cluster analysis for the outcome and transform it into a categorical variable for trajectories (the clusters). Predictors that are time-dependent can also be transformed into trajectories. Then the transformed outcome, a nominal variable, can be used as dependent variable of a non-linear model such as a regression tree; the predictor selection is implicit in the tree-building algorithm. This is likely not implementable in SAS stat alone, as the clustering algorithms are there, but, as far as I know, the regression trees are not part of SAS stat, they are included in the SAS enterprise miner product. The approach can be attempted in R; however, regardless of the software, there are the issues of how many trajectories (clusters) to select, which is not a simple problem, and also what type of tree model to use, as there are many varieties (not sure which are available in SAS enterprise miner). For lack of simpler alternatives, I would suggest a quick-and-dirty approach, albeit imperfect and with risk of bias: in GENMOD you could begin by fixing the correlation structure to exchangeable, and then try a humble backward selection manually, one-at-a-time, using p-values and checking at what point the information criterion (QIC for GEE in GENMOD) is minimized in the backward selection sequence. Select the set of predictors that minimize QIC. Just and idea. ... View more

oh jeez, I might get some heat for this, but I think that as long as you can have some direction for the variables or items that allow you to come up with a reasonable interpretation for the solution, then it's fine to run a principal component analysis or even a factor analysis (with an extraction different than maximum likelihood) with the usual proc factor, because the analysis is exploratory and descriptive. You are not testing anything, all you want to know is whether there are some natural groupings among the items or variables. The factor solution should give you an indication of that. However, the situation is more complicated if you want to do a confirmatory factor analysis. In that case a specified model for the variables is tested and unfortunately I do not think that proc calis offers the most up-to-date methodology for "easily" testing those models with variables that are not continuous (and normally distributed), unless you have a huge sample size. You might have to use Mplus for that, yikes.         ... View more

Proc logistic calculates all the sensitivity and 1-specificity values for the range of cutoff points. That's how it constructs the ROC curve. You can output these values in a datset that will allow you also to calculate PPV and NPV for the range of cutoff points. Assume you have a numerical variable that you are going to use for discriminating between two groups (cases, coded as 1 and non-cases coded as 0). The following statement will plot the ROC curve, and produce a datatset with the components that will let you calculate specificity, sensitivity, PPV and NPV for each of the values of the numerical variable: ods graphics on; proc logistic data=your_data plots(only)=roc(id=obs); model case (event='1')=numerical_variable; score outroc=data_roc; run; ods graphics off; For each cutoff point (i.e. for each value of the numerical variable), sorted in descending order, the dataset data_roc contains the following variables: _sensit_ (Sensitivity) _1mspec_ (1-specificity) _pos_ (frequency of true cases) _neg_ (frequency of true non-cases) _falpos_ (frequency of non-cases wrongly classified as cases) _falneg_ (frequency of cases wrongly classified as non-cases) With these variables you can easily calculate the specificity: Specificity=1-_1mspec_; Now, to calculate PPV and NPV: PPV=_pos_/(_pos_+_falpos_); NPV=_neg_/(_neg_+_falneg_); Unfortunately the "data_roc" does not have the values of the numerical_variable. So you need to sort the original dataset and merge it with the "data_roc" set to have everything together. Then you can plot each measure or conduct other analyses. The sorting and merging would be something like this: proc sort data=your_data; by descending numerical_variable; run; data all; merge your_data data_roc; run; Hopes this helps. ... View more