While there were many study design elements to consider with these possibilities, the pharmacoepidemiology team recommended that the supplemental comparative-effectiveness analysis only include the contemporaneous comparator group. The team’s decision was driven by the consideration of the possibility of confounding3 being introduced to the analysis due to temporal changes in treatment guidelines, available treatments, diagnostic procedures, etc.4
Specifically, the team wanted to reduce the possibility of the observed treatment effect being explained by calendar-time changes rather than the true relative efficacy of the treatments. That said, the team was cognizant that the decision to use a contemporaneous versus a non-contemporaneous cohort is a highly nuanced one, with decisions being made on a case-by-case basis, while taking account of factors such as sample size, clinical practice changes, etc.
If contemporaneous EHR data were insufficient in sample size, it would then be reasonable for them to consider including luzumab data that were collected before and after the clinical-trial period. The inclusion of non-contemporaneous data would also be reasonable if changes in patient-care practices had been minimal over time.
After executing an observational study-design that was deliberate in recognizing and minimizing errors due to chance, selection bias, confounding and measurement error, Eland Pharma was able to show that there was evidence that their drug, vidumab, was likely more effective at treating melanoma than the current standard of care, luzumab.
The HTA body considered this additional evidence to be compelling, granted the drug market access in Zamunda, and included the drug in the formulary for melanoma patients. This decision was welcomed by all! As melanoma patients began receiving the much awaited vidumab, teams at Eland Pharma popped champagne to celebrate a job-well done — “Oh, what you can accomplish with EHR data,” one pharmacoepidemiologist mused.