There is a basic example here: https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.3/statug/statug_phreg_examples08.htm The outcome is time-dependent survival status (Time*VStatus), and that is right - the status can only be 0 (dead) or 1 (alive). The model in example "Time*VStatus(0)=LogBUN HGB" looks at time-dependent probability of survival depending on two factors: LogBUN and HGB. Color would have to be a factor in your example, and it would be useful to "freeze" those additional categories with color transitions before testing the effect of color on survival (i.e., if at any point of time the patient transitioned from purple to blue, h/she will no longer belong to the "purple" category, but to a separate "purple-to-blue" category). ... View more

data mri1 mri2 mri3; set mri; if 0<=wbvy<=9 then do; output mri1; if wbvy ne 9 then do; output mri2; if wbvy ne 8 then do; output mri3; end; end; end; run; ... View more

Yes, although we do not have directionality with causal mediation (but maybe there is - look at all the minus signs in the table!). It is tempting to say that part of the reason why individuals of minority status have lower test scores is because of lower income, but that would need further exploration. Right now, it would probably be more accurate to conclude that race has an effect on test scores partly due to differences in income. ... View more

You are asking what percent of the effect of race on test scores is mediated by differences in income. The answer from the table is 33% (Percentage Mediated) and this is statistically significant (p<0.0001). In any model, the factor is always on the right and the outcome is on the left. The mediator statement shows the mediator model (wealthy = race). Hence, the direct pathway is race -> test scores and the indirect (mediated) pathway is race-> wealth -> test scores. This can be drawn as a triangle with 33% over “->wealth->” and the remaining 66% over the direct “race -> test scores” arrow. ... View more

Here is a workaround e.g., for α : 1. Find/Replace to "alpha" in excel 2. import 3. Replace "alpha" with α in SAS (https://www.lexjansen.com/pharmasug/2010/CC/CC19.pdf) : %macro specchar(n=, symbol=); %global &symbol; data _null_; char=input(&n., $UCS2B4.); call symput("&symbol.",trim(left(char))); run; %put symbol=&&&symbol..; data test; var1 = "alpha"; var2 = tranwrd(var1, "alpha", "&&&symbol.."); run; %mend; %specchar(n='0040'x, symbol=alpha); *this is not exactly alpha, but 'at', the format does not show a symbol unless its code starts with '00' on a character map, you would need to explore that further to make this work; ... View more

Here is another way: https://communities.sas.com/t5/SAS-Programming/Import-data-from-MS-access-Excel-while-retaining-special/m-p/352237#M82077 ... View more

I would use Find/Replace in xcel to change those characters to "_gamma" and "_alpha." ... View more

SAS Miner can split the data into training, testing and validation datasets. This partition can be user-defined: https://support.sas.com/documentation/onlinedoc/miner/casestudy_59123.pdf. Sensitivity parameter shows how well the model identifies positive cases. If “hit” = true positive, and “miss” = false negative, then sensitivity = hits/(hits+misses). A 1:1 hit:miss ratio results in sensitivity of 0.5; 2:1 - sensitivity of 0.66. A sensitivity between 70 and 100% is considered good. ... View more

This macro is from 2016. Maybe it was already made into a SAS procedure? ... View more

There are Chapter quizzes in "SAS Certification Prep Guide": https://www.sas.com/storefront/aux/en/certpgbp/71337_excerpt.pdf ... View more