For the data, you can run in SAS:   data py1; input dob :ddmmyy10. sdate :ddmmyy10. edate :ddmmyy10. id ageatstart ageatend py ; format dob ddmmyy10. sdate ddmmyy10. edate ddmmyy10.; datalines; 05/03/1980 01/01/2005 31/12/2014 1 24 34 10.0 12/08/2006 12/08/2006 31/12/2014 2 0 8 8.39 19/09/1975 01/01/2005 20/12/2011 3 29 35 6.38 ; run;  The confusion re. age groups was due to me giving examples where different cutoff thresholds were used for organising the age group. Obviously, age groups will be organised as consecutive groups (without any overlap); so let's agree that the desired arrangement for age groups should be: agegrp1: <20; agegrp2: [20-29]; agegrp3: [30,39]; and agegrp4: >=40. ... View more

I forgot to mention that it will be used as data for analysis. ... View more

Please see attached; I would like the 'py' (i.e. the total person time a person is contributing) to be distributed across the four agegrp variables/column. Thanks again. ... View more

Let me explain a bit further with a simple example. Let's consider a participant (partA) who was born in 1980, entered the study in 2000 and stopped participating in it in 2020 (e.g. dob=1.1.1980 sdate=1.1.2000 edate=1.1.2020). This person contributed a total of 20 person years in the study (i.e. edateb - sdate). For my analysis, let's also say that I am organising the participants (according to their age) into 3 age groups, i.e. agegroup1: <25; agegroup2: [25-34); and agegroup3: >=35. The 20 years of total person time partA contributed to the study are distributed across these 3 age groups, i.e. 5 years in agegroup1, 10 years in agegroup2 and 5 years in agegroup3 (since during the 20 years of their participation partA 'moved' from agegroup1 to agegroup2 and to agegroup3).   I hope this is clearer now, thanks in advance!     ... View more