title: Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers.
abstract: OBJECTIVE
  Record linkage can enhance data quality of observational database studies. Probabilistic linkage, a method that allows partial match of linkage variables, overcomes disagreements arising from errors and omissions in data entry but also results in false-positive links. The study aimed to assess the validity of probabilistic linkage in the absence of unique personal identifiers (UPI) and the methods of cutoff weight selection.
  
  
  METHODS
  We linked an implantable cardioverter defibrillator placement registry to Medicare inpatient files of 1 year with anonymous nonunique variables and assessed the validity of three methods of cutoff selection against an internally derived gold standard with UPI.
  
  
  RESULTS
  Of the 64,890 registry records with an expected linkage rate of 55-65%, 55% were linked at cutoffs associated with positive predictive value (PPV) of ≥90%. Histogram inspection suggested an approximate range of optimal cutoffs. The duplicate method made accurate estimates of cutoff and PPV if the method's assumption was met. With adjusted estimates of the sizes of true matches and searched files, the odds formula method made relatively accurate estimates of cutoff and PPV.
  
  
  CONCLUSIONS
  Probabilistic linkage without UPI generated valid linkages when an optimal cutoff was chosen. Cutoff selection remains challenging; however, histogram inspection, the duplicate method, and the odds formula method can be used in conjunction when a gold standard is not available.
authors.affiliation: Department of Biostatistics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: Chih-Ying
authors.initials: CY
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Matsuyama
authors.affiliation: Department of Biostatistics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.|Department of Integrated Science and Engineering for Sustainable Society, Chuo University, Tokyo, Japan.
authors.forename: Yasuo
authors.initials: Y
authors.lastname: Ohashi
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Franklin
authors.affiliation: Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.|Department of Pharmacoepidemiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Soko
authors.initials: S
authors.lastname: Setoguchi
mesh_terms: D000368:Aged; D016208:Databases, Factual / Q000706:statistics & numerical data*; D017147:Defibrillators, Implantable / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients; D008498:Medical Record Linkage / Q000379:methods*; D006278:Medicare; D011237:Predictive Value of Tests; D011336:Probability; D012042:Registries; D014481:United States
keywords: Medicare; cutoff; database; pharmacoepidemiology; probabilistic model; record linkage; registry