ArticlePharmacoepidemiology and drug safety2016
More realistic power estimation for new user, active comparator studies: an empirical example.
Article in Pharmacoepidemiology and drug safety, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Impact of study design decisions on identification of treatment initiators of medications for opioid use disorder.Addiction (Abingdon, England) · 2026Article
- Re-evaluating Safety and Effectiveness of Dabigatran Versus Warfarin in a Nationwide Data Environment: A Prevalent New-User Design Study.Drugs - real world outcomes · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposePharmacoepidemiologic 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.
methodsWe 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.
resultsThere 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%).
conclusionsIn 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.
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