title: Sampling patients within physician practices and health plans: multistage cluster samples in health services research.
abstract: OBJECTIVE
  To better inform study design decisions when sampling patients within health plans and physician practices with multiple analysis goals.
  
  
  METHODS
  Chronic eye care patients within six health plans across the United States.
  
  
  METHODS
  We developed a simulation-based approach for designing multistage samples. We created a range of candidate designs, evaluated them with respect to multiple sampling goals, investigated their tradeoffs, and identified the design that is the best compromise among all goals. This approach recognizes that most data collection efforts have multiple competing goals.
  
  
  METHODS
  We constructed a sample frame from all diabetic patients in six health plans with evidence of chronic eye disease (glaucoma and retinopathy).
  
  
  RESULTS
  Simulations of different study designs can uncover efficiency gains as well as inform potential tradeoffs among study goals. Simulations enable us to quantify these efficiency gains and to draw tradeoff curves.
  
  
  CONCLUSIONS
  When designing a complex multistage sample it is desirable to explore the tradeoffs between competing sampling goals via simulation. Simulations enable us to investigate a larger number of candidate designs and are therefore likely to identify more efficient designs.
authors.affiliation: RAND Health, Santa Monica, CA 90407-3297, USA.
authors.forename: John L
authors.initials: JL
authors.lastname: Adams
authors.forename: Steven L
authors.initials: SL
authors.lastname: Wickstrom
authors.forename: Margaret J
authors.initials: MJ
authors.lastname: Burgess
authors.forename: Paul P
authors.initials: PP
authors.lastname: Lee
authors.forename: José J
authors.initials: JJ
authors.lastname: Escarce
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D003930:Diabetic Retinopathy; D004526:Efficiency; D005901:Glaucoma; D006040:Goals; D006302:Health Services Research / Q000379:methods*; D006801:Humans; D018579:Patient Selection; D017061:Prepaid Health Plans; D014481:United States