title: More realistic power estimation for new user, active comparator studies: an empirical example.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, USA.
authors.forename: Mugdha
authors.initials: M
authors.lastname: Gokhale
authors.affiliation: Department of Medicine, University of North Carolina School of Medicine, USA.
authors.forename: John B
authors.initials: JB
authors.lastname: Buse
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, USA.
authors.forename: Virginia
authors.initials: V
authors.lastname: Pate
authors.affiliation: Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, USA.
authors.forename: M Alison
authors.initials: MA
authors.lastname: Marquis
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, USA.
authors.forename: Til
authors.initials: T
authors.lastname: Stürmer
mesh_terms: D000368:Aged; D015982:Bias; D054873:Dipeptidyl-Peptidase IV Inhibitors / Q000009:adverse effects / Q000627:therapeutic use; D015340:Epidemiologic Research Design; D005500:Follow-Up Studies; D006801:Humans; D007004:Hypoglycemic Agents / Q000009:adverse effects* / Q000627:therapeutic use; D015994:Incidence; D006278:Medicare; D010190:Pancreatic Neoplasms / Q000453:epidemiology* / Q000209:etiology; D017891:Pharmacoepidemiology / Q000379:methods*; D016016:Proportional Hazards Models; D018401:Sample Size; D013453:Sulfonylurea Compounds / Q000009:adverse effects; D045162:Thiazolidinediones / Q000009:adverse effects / Q000627:therapeutic use; D013997:Time Factors; D014481:United States
chemical_list: D054873:Dipeptidyl-Peptidase IV Inhibitors; D007004:Hypoglycemic Agents; D013453:Sulfonylurea Compounds; D045162:Thiazolidinediones
keywords: bias; new-user design; pharmacoepidemiology; power; sample size