Dead and Trtsize are the DEPENDENT variables whose predicted value PROC LOGISTIC estimates.  If any of the independent variables were missing from an observation, PROC LOGISTIC could NOT estimate its predicted value. ... View more

Does including in the original logistic regression a "dummy" observation with a value of DOSE=6.5 but missing values for DEAD and TRTSIZE yield a predicted value and its 95% confidence interval for that DOSE? ... View more

The odds ratio of zero for the variable, MORBIDOBESITY, implies that none of the study subjects meeting the WHERE condition of      PCI_POSTCATH=1 and POSTCATHCOMPLI1=1 and FEMALE=1. died.  So, this is a sample size issue. You can include the MODEL statement option, FIRTH, following a forward slash ("/") after the independent variables to see one approach for dealing with this issue.  Another approach is to increase your sample size.  A third approach is to make your WHERE condition less restrictive. I agree with Reeza that you should use PROC SURVEYLOGISTIC instead of PROC LOGISTIC when your sample respondents have weights. ... View more

Transpose twice, the first time using UNIT as the VAR and the word, UNIT, as the prefix, the second time using VOLUME as the VAR and the word, VOLUME, as the prefix.  Then, merge the two transposed output data sets. ... View more

The current version of PROC GLIMMIX (9.3) allows only univariate responses (for example, PCRVU).  As far as I know, only PROC GLM allows multivariate responses. If what you want is somewhat equivalent to the R-squared statistic in multiple linear regression to determine "the percentage of the variability in the dependent variable, PCRVU, 'explained' by the independent variables in the regression model", then you might Google the phrase, "variable importance", to look at statistical studies that have tried to tease out the contribution of model variables in "explaining" the variability of the dependent variable.  Quasi-R-squared statistics and variable importance statistics are also available for logistic regression and proportional hazards regression. A crude, not fully acceptable, method for doing this for your application might be the following:   1. Run a regression model with all the disease variables and other potentially confounding variables to determine          the maximum R-squared statistic "explaining" the variability in the dependent variable for this model.   2. Run a regression model with only the other potentially confounding variables but none of the disease variables          to determine the minimum R-squared statistic "explaining" the variability in the dependent variable by these other variables          for this model.   3. Run 30 other regression models with the potentially confounding variables and all but one of each of the 30 disease variables          to determine the R-squared statistics "explaining" the variability in the dependent variable by the potentially confounding variables          and 29 of the 30 disease variables.   4. Calculate the difference between the maximum R-squared statistic from model #1 and each of the 30 R-squared statistics from          the 30 model #3's.   5. Calculate the difference between the maximum R-squared statistic from model #1 and the minimum R-squared statistic in model #2.   6. Use either the calculated difference from #4 above or that difference divided by the difference from #5 above as your measure          of the "contribution" of a specific disease type to the variation in your dependent variable. A problem with this method is that the sum of these contributions for all 30 disease types may  not equal and may even exceed the difference from #5 above. By the way, the suggestion in the previous e-mail was only to reduce the number of disease types or clusters of disease types that you would have to study. ... View more

I don't know whether the following would work, but you might consider it. In a DATA step before the PROC GLM paragraph, you might construct three new variables from the TRT that contain the three treatment contrasts of interest to you.  Then, use those three new variables instead of TRT in PROC GLM using the LSMEANS statement with these three new variables and the SIMULATE option to calculate significance probabilities that account for unequal variances among the different treatment groups (recommended by Westfall PH, Tobias RD, Wolfinger RD.  Multiple comparisons and multiple tests using SAS.  Cary, NC:  SAS Institute, Inc., 2011, Chapter 5.2):     data new_treatmentdata;         set treatmentdata;         * Create new variables for the treatment contrasts of interest;         trt12=0;         trt34=0;         trt56=0;         select;              when (trt eq 1) trt12=-1;              when (trt eq 2) trt12=1;              when (trt eq 3) trt34=-1;              when (trt eq 4) trt34=1;              when (trt eq 5) trt56=-1;              when (trt eq 6) trt56=1;              otherwise do;                   trt12=.;  trt34=.;  trt56=.;              end;        end;        output new_treatmentdata;        label trt12="Contrast treatments 1 and 2";        label trt34="Contrast treatments 3 and 4";        label trt56="Contrast treatments 5 and 6";     run;     proc glm data=new_treatmentdata;          class trt12 trt34 trt56;          model response=trt12 trt34 trt56 / solution;          means trt12 trt34 trt56 / hovtest=levene welch;          lsmeans trt12 trt34 trt56 / adjust=simulate(nsamp=50000 seed=7654321 report);     run;     quit; ... View more