title: The cost-effectiveness of reclassification sampling for prevalence estimation.
abstract: BACKGROUND
  Typically, a two-phase (double) sampling strategy is employed when classifications are subject to error and there is a gold standard (perfect) classifier available. Two-phase sampling involves classifying the entire sample with an imperfect classifier, and a subset of the sample with the gold-standard.
  
  
  RESULTS
  In this paper we consider an alternative strategy termed reclassification sampling, which involves classifying individuals using the imperfect classifier more than one time. Estimates of sensitivity, specificity and prevalence are provided for reclassification sampling, when either one or two binary classifications of each individual using the imperfect classifier are available. Robustness of estimates and design decisions to model assumptions are considered. Software is provided to compute estimates and provide advice on the optimal sampling strategy.
  
  
  CONCLUSIONS
  Reclassification sampling is shown to be cost-effective (lower standard error of estimates for the same cost) for estimating prevalence as compared to two-phase sampling in many practical situations.
authors.affiliation: Department of Mathematics, Hope College, Holland, Michigan, United States of America.
authors.forename: Airat
authors.initials: A
authors.lastname: Bekmetjev
authors.forename: Dirk
authors.initials: D
authors.lastname: VanBruggen
authors.forename: Brian
authors.initials: B
authors.lastname: McLellan
authors.forename: Benjamin
authors.initials: B
authors.lastname: DeWinkle
authors.forename: Eric
authors.initials: E
authors.lastname: Lunderberg
authors.forename: Nathan
authors.initials: N
authors.lastname: Tintle
mesh_terms: D002965:Classification / Q000379:methods*; D003362:Cost-Benefit Analysis; D006801:Humans; D015995:Prevalence; D012680:Sensitivity and Specificity; D012984:Software / Q000191:economics*; D060388:Support Vector Machine