If I look at all people born in 1960, living in NY, covered by BCBS, that’s a like group.
If I then split by “had a colonoscopy between 2008 and 2012, inclusive” vs “didn’t” and look at 2012 through 2021 outcomes to draw conclusions, it’s possible that that filtering makes them unlike groups (I mean, it definitionally does in at least the primary selection criteria). Given that the effort is approximately that of a SQL query, I’d be interested to know if there’s a possible signal there, which would need to be corroborated with other data sets to determine the repeatability of the correlation and then if there’s any likely causal link.
If I then split by “had a colonoscopy between 2008 and 2012, inclusive” vs “didn’t” and look at 2012 through 2021 outcomes to draw conclusions, it’s possible that that filtering makes them unlike groups (I mean, it definitionally does in at least the primary selection criteria). Given that the effort is approximately that of a SQL query, I’d be interested to know if there’s a possible signal there, which would need to be corroborated with other data sets to determine the repeatability of the correlation and then if there’s any likely causal link.