A couple pharmacokinetics questions:
1) how long do you expect the protective effect of the cardioTx drug to last? Only while the person is still taking it?
2) do you expect that the drug is immediately protective or does it take some amount of time / number of doses for protection to be achieved?
If the drug is expected to be ~immediately protective and only protective while the person continues to take it, then really there's no difference between a person who starts it early (i.e., at the time of first chemo) or later on (prior to any CVA). In this case, I would suggest that time starts at first dose of cardioTx and ends at first of either stoppage of that drug, however you define that, or CVA. That's the simplest approach.
Alternatively, if you think the drug is ONLY protective if it's received right at the beginning - in other words, that in order to prevent heart damage that might lead to a later cardiovascular accident, a person needs to begin the protective therapy at the same time (or before) the start of chemo - then you could run this as an intent-to-treat (ITT) study where you ignore the fact that some people start cardioTx later on. There'd be no censoring of people who start late. If you really want to treat this as a time-varying covariate, where people can be on and off cardioTx at different time intervals, then you'd need to create a "counting process" data structure (or the horizontal equivalent) and use the PHREG syntax for that (you can find this by google). In this case, a person can contribute to more than one cohort. This is certainly doable, but results are not simple to interpret, and you will need to consider whether you're allowing the possibility of multiple CVA events per person or censoring at the first event - this will determine whether you need to use the ID statement in PHREG, I believe.
If you really only care about comparing people who receive cardioTx *at the time of first chemo* vs. those who do not receive treatment, then one possibility is to censor people who start off without cardioTx and then receive it later (that is, censor such people at the date they receive cardioTx). However, that is definitely prone to introducing bias unless you have some sense that the day of starting cardioTx (relative to chemo) is random. Have you looked at predictors of early receipt (day 0) receipt of cardioTx, i.e.: proc logistic data=cardio descending;
model early_cardioTx = <predictors>;
run;
...where predictors might be things like demographics, socioeconomics, comorbidities, etc.
Another possibility (to enhance at least the first, simple or ITT approaches above) is to use inverse propensity of treatment weights (IPTW), where the weights are essentially the inverse of the probabilities that would be output (using ODS) by the logistic model above. In fantasy-land, at least, applying these weights to people and then running one of the models above simulates a clinical trial in which you, for instance, randomized people to early cardioTx vs. not and then followed them to look for CVA.
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