More realistic power estimation for new user, active comparator studies: an empirical example.

PURPOSE: Pharmacoepidemiologic studies are often expected to be sufficiently powered to study rare outcomes, but there is sequential loss of power with implementation of study design options minimizing bias. We illustrate this using a study comparing pancreatic cancer incidence after initiating dipeptidyl-peptidase-4 inhibitors (DPP-4i) versus thiazolidinediones or sulfonylureas. METHODS: We identified Medicare beneficiaries with at least one claim of DPP-4i or comparators during 2007-2009 and then applied the following steps: (i) exclude prevalent users, (ii) require a second prescription of same drug, (iii) exclude prevalent cancers, (iv) exclude patients age <66 years and (v) censor for treatment changes during follow-up. Power to detect hazard ratios (effect measure strongly driven by the number of events) ≥ 2.0 estimated after step 5 was compared with the naïve power estimated prior to step 1. RESULTS: There were 19,388 and 28,846 DPP-4i and thiazolidinedione initiators during 2007-2009. The number of drug initiators dropped most after requiring a second prescription, outcomes dropped most after excluding patients with prevalent cancer and person-time dropped most after requiring a second prescription and as-treated censoring. The naïve power (>99%) was considerably higher than the power obtained after the final step (~75%). CONCLUSIONS: In designing new-user active-comparator studies, one should be mindful how steps minimizing bias affect sample-size, number of outcomes and person-time. While actual numbers will depend on specific settings, application of generic losses in percentages will improve estimates of power compared with the naive approach mostly ignoring steps taken to increase validity.