Li C, Glüer CC, Eastell R, Felsenberg D, Reid DM, Roux C, Lu Y. Tree-structured subgroup analysis of receiver operating characteristic curves for diagnostic tests.
Acad Radiol 2012;
19:1529-36. [PMID:
23122572 PMCID:
PMC8076100 DOI:
10.1016/j.acra.2012.09.007]
[Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 09/16/2012] [Accepted: 09/18/2012] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES
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.
MATERIALS AND 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.
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