Hi Folks:
I'm conducting survival analyses and agecat: one of categorical variables crossed on the KM survival curves. Does it invalidate the assumption of proportional hazard and stop me for a Cox proportional HM? two other categorical variables: sex and comorb (1,0) don't have this issue.
Thank you for your time and help.
proc sort data=pti4;
by sex agecat comorb; run;
proc phreg data=pti4;
class sex1(ref='1') agecat(ref='2') comorb(ref='0')/param=ref order=internal;
model duration*vital_status(0)= sex1 agecat comorb/ties=Efron;
run;
It does invalidate the assumption, but your 90+ group being much smaller is causing the issue. Can you treat age as continuous instead?
the age variable is categorical to begin with such as: 0, 10s, 20s, 30s, 40s, 50s, 60s, 70s, 80s, 90s and 10s. I have no events until 30s and very few events in some age groups. See attached. In the snippet, how agecat is created from 10-year age group.
if age1=. then agecat=.;
if age1 in (0,1,2) then agecat=1; else
if age1 in (3,4) then agecat=2; else
if age1 in (5,6) then agecat=3; else
if age1 in (7,8) then agecat=4;
if age1 in (9,10) then agecat=5;
It seems like that's an arbitrary decision then. In that case I'd like recommend using 80+ instead of 90+ and I suspect that will help. Depending on what you're measuring the other usual suggestion is to make it a time dependent covariate, which sort of makes sense for age since it does not stay the same over time anyways.
@Cruise wrote:
how do you make age a time dependent variable? my date variable in the data is 'date' by day unit, so: age(10-year group)*date as an interaction? or use the agecat*date?
Bear with me please, if my wild guess doesn't make sense 🙂
Regrouping age as below solved the crossing in the age-groups in KM curves.
if age1=. then agec=.;
if age1 in (0,1,2) then agec=1; else
if age1 in (3,4) then agec=2; else
if age1 in (5,6) then agec=3; else
if age1 in (7,8,9,10) then agec=4;
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
Learn how use the CAT functions in SAS to join values from multiple variables into a single value.
Find more tutorials on the SAS Users YouTube channel.