That is correct -- GENMOD does not estimate/update the correlation matrix when TYPE=FIXED or USER is specified. ... View more

The confidence limits for the predicted probabilities are computed as shown in the "Details: Linear Predictor, Predicted Probability, and Confidence Limits" section of the LOGISTIC documentation: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/logistic_toc.htm ... View more

You can get the Cox and Snell and the Nagelkerke R-squares by simply adding the RSQUARE option in your MODEL statement. This option can be used with ordinal models. However, these R-squares don't really have the same "variance explained' interpretation as in ordinary least squares regression. They can be used to compare competing models. The ROC curve and the area under it are only defined for the binary response case and so are not available for the ordinal model. ... View more

Glen Walker's and Jack Shostak's book "Common Statistical Methods for Clinical Research with SAS Examples, Third Edition is an excellent book that covers essentially all of this with chapters on each of those topics and several others. While the title mentions clinical research, the presentation is just a good, basic treatment of many basic statistical methods with lots of examples. See more here: http://www.sas.com/apps/pubscat/bookdetails.jsp?catid=1&pc=62004 ... View more

Use the N= option to control the number of resamples generated (default is 20,000) and the OUTSAMP= option to create a data set containing all of the generated samples. See the MULTTEST documentation: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/multtest_toc.htm ... View more

If you are looking get a permutation-adjusted t-test, you can use PROC MULTTEST which will do the permutations internally for you and give you a permutation-adjusted p-value computed from the p-values of all the permutations. For example: data a; input Placebo VitA; Grp="Placebo"; Y=Placebo; output; Grp="VitA"; Y=VitA; output; keep Grp Y; datalines; 8.62 0.06 1.48 1.72 8.93 2.19 9.57 7.32 2.65 7.53 ; proc multtest data=a perm; class Grp; test mean(Y); run; See the MULTTEST documentation for details on the computations and examples: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/multtest_toc.htm ... View more

If you ignore positive correlation within clusters you will underestimate the standard errors of time-independent predictors and overestimate the standard errors of time-dependent predictors. The opposite is true for negative correlation. ... View more

It sounds like you just need to use events/trials syntax and then specify institutions as the SUBJECT= effect indicating that the sets of trials are correlated within institutions. The data would contain one observation for each quarter. For example, if you assume an AR correlation structure across quarters: proc genmod class instID quarter group; model NumPos / NumSubj = group; repeated subject=instID / type=AR within=quarter; run; ... View more

PROC GENMOD uses the same data arrangement as PROC MIXED -- one response variable with multiple observations containing the repeated measurements of each subject. This is illustrated in the examples in the GENMOD documentation: 9.1: http://support.sas.com/onlinedoc/913/getDoc/en/statug.hlp/genmod_index.htm 9.2: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/genmod_toc.htm You can use any of the response distributions offered by the DIST= option including the continuous distributions such as normal, gamma, etc. ... View more

You can use the STRATA statement in PROC LOGISTIC for this without needing to use PROC PHREG anymore. See the "Conditional Logistic Regression for Matched Pairs Data" example in the LOGISTIC documentation: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/logistic_toc.htm ... View more

A test of the polychoric correlation is not provided. However, if you add the CL option in the TABLE statement you will get a 95% confidence interval for the correlation. If this interval does not contain the hypothesized value, then you could consider the hypothesis rejected. ... View more

Your GENMOD code probably is not doing what you expect. When you specify a link and DIST=NORMAL, GENMOD still assumes that the response itself (not transformed) is normal and then models g(y) where g(.) is the link function. If you want to transform the response to be normal and then model that normalized response, do the transformation in a DATA step and then use a procedure (GLM, MIXED, or GENMOD with DIST=NORMAL) that models the transformed response assuming it is normally distributed. An alternative in your case would be to try a nonnormal distribution (maybe gamma) in GENMOD for your QOL1 response. ... View more