Hello,
I am conducting a retrospective cohort study to determine the association between receiving medicine X with death during the first 100 days of therapy for patients with leukemia, from 2010-2020. Starting in 2015, all patients began receiving medicine X on Day 10 (t=10) of therapy to prevent infection, whereas nobody received it before. So, I have two cohorts to compare: those receiving medicine X, and those who didn’t.
Assuming era-dependent confounders are controlled for (e.g., quality of supportive care in each era, etc.), I’d like to determine the effect of medicine X on survival using a Cox PH model, but I am faced with the problem that many patients (10% of total cohort, 20% of events) die before day 10 and therefore die before they can receive medicine X (the exposure). As expected, this survivorship bias contributes to a large association between survival to 100 days and medicine X.
What strategy do you recommend to approach the issue of survivorship bias here, allowing for basic limitations to retrospective, non-randomized studies? Given that patients aren’t at risk of the event (death given medication X status) until they actually receive medication X, my first instinct is to simply left-truncate the data and describe the effect of medicine X as the hazard of death given survival to Day 10 (when they receive medicine X), assuming that confounders between those receiving medication X and those surviving are controlled for. Are there any other approaches I should consider? I considered a time-varying exposure, but I think it is inappropriate here given the specifics of medication X and the specific disease here, as the effect of medication X is expected to be quite different in days 1-10 than it would be in days >10, and I’m not interested in that question right now.
Thanks