Dear Statalist forum,
I have a study comparing users of drug X with non-users. This will be analysed with exposure to drug X as a time-dependent (time-varying) variable – where person-time for all individuals after study entry will be classified as unexposed until their first prescription for drug X; thereafter all person-time will be classified as exposed. This is to minimise immortal time bias.
I need help correctly structuring my data prior to time-dependent cox regression. My thoughts are that my data needs to be expanded into several rows per ID, with survival-time varying by classification of unexposed/exposed if an individual changes from unexposed to exposed after study entry...
A sample is below. Any help will be much appreciated.
I have a study comparing users of drug X with non-users. This will be analysed with exposure to drug X as a time-dependent (time-varying) variable – where person-time for all individuals after study entry will be classified as unexposed until their first prescription for drug X; thereafter all person-time will be classified as exposed. This is to minimise immortal time bias.
I need help correctly structuring my data prior to time-dependent cox regression. My thoughts are that my data needs to be expanded into several rows per ID, with survival-time varying by classification of unexposed/exposed if an individual changes from unexposed to exposed after study entry...
A sample is below. Any help will be much appreciated.
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float ID int entry byte exposure int exposure_date float(outcome exitdate) 1 17267 1 17281 0 22585 2 21920 1 21920 0 22585 3 19703 0 . 0 20380 4 19684 0 . 0 22586 5 14656 1 14839 1 20306 6 17251 1 17489 0 22581 7 19523 1 19575 0 22586 8 19564 0 . 0 19814 9 19667 0 . 0 19848 10 17210 1 19253 0 22586 end format %td entry format %td exposure_date format %td exitdate
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