title: Tree-structured subgroup analysis of receiver operating characteristic curves for diagnostic tests.
abstract: OBJECTIVE
  Multiple diagnostic tests are often available for a disease. Their diagnostic accuracy may depend on the characteristics of testing subjects. The investigators propose a new tree-structured data-mining method that identifies subgroups and their corresponding diagnostic tests to achieve the maximum area under the receiver-operating characteristic curve.
  
  
  METHODS
  The Osteoporosis and Ultrasound Study is a prospectively designed, population-based European multicenter observational study to evaluate state-of-the-art diagnostic methods for assessing osteoporosis. A total 2837 women underwent dual x-ray absorptiometry (DXA) and quantitative ultrasound (QUS). Prevalent vertebral fractures were determined by a centralized radiology laboratory on the basis of radiographs. The data-mining algorithm includes three steps: defining the criteria for node splitting and selection of the best diagnostic test on the basis of the area under the curve, using a random forest to estimate the probability of DXA being the preferred diagnostic method for each participant, and building a single regression tree to describe subgroups for which either DXA or QUS is the more accurate test or for which the two tests are equivalent.
  
  
  RESULTS
  For participants with weights ≤54.5 kg, QUS had a higher area under the curve in identifying prevalent vertebral fracture. For participants whose weights were >58.5 kg and whose heights were ≤167.5 cm, DXA was better, and for the remaining participants, DXA and QUS had comparable accuracy and could be used interchangeably.
  
  
  CONCLUSIONS
  The proposed tree-structured subgroup analysis successfully defines subgroups and their best diagnostic tests. The method can be used to develop optimal diagnostic strategies in personalized medicine.
authors.affiliation: School of Mathematics and Computational Science, Sun Yet-Sen University, Guangzhou, Guangdong, China.
authors.forename: Caixia
authors.initials: C
authors.lastname: Li
authors.forename: Claus-C
authors.initials: CC
authors.lastname: Glüer
authors.forename: Richard
authors.initials: R
authors.lastname: Eastell
authors.forename: Dieter
authors.initials: D
authors.lastname: Felsenberg
authors.forename: David M
authors.initials: DM
authors.lastname: Reid
authors.forename: Christian
authors.initials: C
authors.lastname: Roux
authors.forename: Ying
authors.initials: Y
authors.lastname: Lu
mesh_terms: D015502:Absorptiometry, Photon; D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D015519:Bone Density; D003663:Decision Trees; D005260:Female; D006621:Hip Joint / Q000000981:diagnostic imaging; D006801:Humans; D008875:Middle Aged; D015663:Osteoporosis, Postmenopausal / Q000000981:diagnostic imaging*; D012372:ROC Curve; D012044:Regression Analysis; D016103:Spinal Fractures / Q000000981:diagnostic imaging*; D014463:Ultrasonography; D055815:Young Adult