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Tree-structured subgroup analysis of receiver operating characteristic curves for diagnostic tests
Caixia Li1, Claus-C Glüer, Richard Eastell
1School of Mathematics and Computational Science, Sun Yet-Sen University, Guangzhou, Guangdong, China.
Academic Radiology
|November 6, 2012
Summary
A new data-mining method identifies subgroups for optimal osteoporosis diagnosis. Quantitative ultrasound (QUS) is better for lighter women, while dual x-ray absorptiometry (DXA) is preferred for specific heavier/shorter women.
Area of Science:
- Biostatistics
- Medical Informatics
- Radiology
Background:
- Diagnostic accuracy of tests can vary based on patient characteristics.
- Osteoporosis diagnosis often involves multiple tests like dual x-ray absorptiometry (DXA) and quantitative ultrasound (QUS).
Purpose of the Study:
- To develop a tree-structured data-mining method for identifying patient subgroups.
- To determine the optimal diagnostic test (DXA or QUS) for each subgroup to maximize diagnostic accuracy, measured by the area under the receiver-operating characteristic curve (AUC).
Main Methods:
- A population-based European multicenter observational study (Osteoporosis and Ultrasound Study) involving 2837 women.
- Data-mining algorithm using random forests and regression trees to analyze DXA and QUS data against prevalent vertebral fractures.
- Defining node-splitting criteria and selecting the best diagnostic test based on AUC.
Main Results:
- The method successfully identified subgroups with differential diagnostic test performance.
- For women weighing ≤54.5 kg, QUS showed a higher AUC for identifying vertebral fractures.
- For women weighing >58.5 kg and height ≤167.5 cm, DXA was superior; others had comparable test accuracy.
Conclusions:
- Tree-structured subgroup analysis effectively defines subgroups and their optimal diagnostic tests.
- This approach facilitates the development of personalized diagnostic strategies for conditions like osteoporosis.
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