title: Design of cluster-randomized trials of quality improvement interventions aimed at medical care providers.
abstract: BACKGROUND
  Randomized trials aimed at improving the quality of medical care often randomize the provider. Such trials are frequently embedded in health care systems with available automated records, which can be used to enhance the design of the trial.
  
  
  METHODS
  We consider how available information from automated records can address each of the following concerns in the design of a trial: whether to randomize individual providers or practices; clustering of outcomes among patients in the same practice and its impact on study size; expected heterogeneity in adherence and the response to the intervention; eligibility criteria and the trade-offs between generalizability and internal validity; and blocking or matching to alleviate covariate imbalance across practices.
  
  
  RESULTS
  Investigators can use available information from an automated database to estimate the amount of clustering of patients within providers and practices, and these estimates can inform the decision on whether to randomize at the level of the patient, the provider, or the practice. We illustrate calculation of the anticipated design effect for a proposed cluster-randomized trial and its implications for sample size. With available claims data, investigators can apply focused eligibility criteria to exclude subjects and providers with expected low compliance or lower likelihood of benefit, although possibly at some loss of generalizability. Chance imbalances in covariates are more likely when randomization occurs at the level of the practice than at the level of the patient, so we propose a matching score to limit such imbalances by design.
  
  
  CONCLUSIONS
  Challenges to compliance, expected small effects, and covariate imbalances are particularly likely in cluster-randomized trials of quality improvement interventions. When such trials are embedded in medical systems with available automated records, use of these data can enhance the design of the trial.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA. rglynn@rics.bwh.harvard.edu
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Glynn
authors.forename: M Alan
authors.initials: MA
authors.lastname: Brookhart
authors.forename: Margaret
authors.initials: M
authors.lastname: Stedman
authors.forename: Jerry
authors.initials: J
authors.lastname: Avorn
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Solomon
mesh_terms: D000328:Adult; D000368:Aged; D016000:Cluster Analysis; D003625:Data Collection / Q000379:methods*; D015981:Epidemiologic Factors; D005260:Female; D006302:Health Services Research / Q000379:methods; D006801:Humans; D008297:Male; D008331:Management Information Systems / Q000706:statistics & numerical data; D008875:Middle Aged; D015337:Multicenter Studies as Topic / Q000379:methods; D010024:Osteoporosis / Q000517:prevention & control; D010349:Patient Compliance / Q000706:statistics & numerical data; D011785:Quality Assurance, Health Care / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012107:Research Design; D018401:Sample Size; D014481:United States