I am performing a survival analysis on medical registry data and would like to model drug exposure to estimate a continuous dose-response curve. I have drug data like this:
where ATC is the unqiue code of the drug, and DDD_pack is the number of days the pack of medication will cover assuming the person is taking the standard dose for that particular drug. Actual dose is unknown.
I'm having problems deciding the best way to construct the exposure variable for the dose response. My initial thoughts are to somehow include DDD pack as a time varying variable in a Cox model. However, I am unsure of how to decide on the time step / interval periods. For example, I could consider interval periods of length 90 days and compute the total DDD out of those 90 days as a proportion. Or I could chose 180 day intervals, or shorter ones. Is there a way to decide? There is also the problem that some people may actually be on a smaller or larger dose than the "standard" and hence the "real" DDD_pack might be somewhat less than or larger than the estimated values in the table. I thought perhaps of looking for patterns in the date dispensed column but it then got confusing since some people might be taking several drugs at once. I would very much appreciate some advice for my analysis, in particlar how to chose the interval / period length for constructing the time depednant variable (drug exposure) for the Cox model.