Hi,   I have a data set where one real outcome variable (binary variable 0 or 1) and the model predicted binary variable (0 or 1).   But I have 60 number of the model predicted binary variable in the data set, which means I need to create the 2*2 table with the actual outcome and each of the model predicted variable to compute the sensitivity, specificity, and youden's index.   Here is the screen shot of my data. In the below data set, allfrc is my real outcome variable and lnn_naa1, lnn_naa2,....., are my model predicted outcome.     I used the following command to compute the sensitivity and sensitivity.    proc sort data=mydata out=dataout; by descending lnn_naa1 descending allfrc; run; proc freq data = dataout order=data; table lnn_naa1*allfrc / senspec ; test kappa; run;   My questions are as below.   1) I created this as macro and run with each model predicted outcome variable (for example, lnn_naa1 * allfrc, and then lnn_naa2*allfrc). But this way takes a lot of time to run.   2) Any recommendation to run the actual outcome * model predicted outcome with calculating the sensitivity, specificity and youden's index?   Thank you for any help and comments!   ... View more

Hi,   I have a data set that I want to loop based on the column base.    I only have the ID column along with other covariates. Here is the output that I want to have.   For example, I want to create the column, names as Index_n3, Index_n2.9, ......, Index_2.9, Index_3.0.   The index column is ranged from -3 to 3 by 0.1 or 0.01. For example for the column Index_n3, I want to have -3 in each observation for the whole observation and second column (Index_n2.9  with -2.9 for the whole observations, up to the range 3.0)   I kind of know how to create the value in the row basis (with do index = -3 to 3 by 0.01), but not sure for the column basis.   I appreciate any help I can get for this problem. Thank you.   ID Index_n3 Index_n2.9 Index_n2.9 ……… Index_2.8 Index_2.9 Index3.0 1 -3 -2.9 -2.8 ……… 2.8 2.9 3 2 -3 -2.9 -2.8 ……… 2.8 2.9 3 3 -3 -2.9 -2.8 ……… 2.8 2.9 3 4 -3 -2.9 -2.8 ……… 2.8 2.9 3 5 -3 -2.9 -2.8 ……… 2.8 2.9 3 6 -3 -2.9 -2.8 ……… 2.8 2.9 3 7 -3 -2.9 -2.8 ……… 2.8 2.9 3 8 -3 -2.9 -2.8 ……… 2.8 2.9 3 9 -3 -2.9 -2.8 ……… 2.8 2.9 3 10 -3 -2.9 -2.8 ……… 2.8 2.9 3 ... View more