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Computerized adaptive testing with decision regression trees: an alternative to item response theory for quality of life measurement in multiple sclerosis

Authors Michel P, Baumstarck K, Loundou A, Ghattas B, Auquier P, Boyer L

Received 11 January 2018

Accepted for publication 31 March 2018

Published 19 June 2018 Volume 2018:12 Pages 1043—1053

DOI https://doi.org/10.2147/PPA.S162206

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristina Weinberg

Peer reviewer comments 2

Editor who approved publication: Dr Johnny Chen


Pierre Michel,1,2 Karine Baumstarck,1 Anderson Loundou,1 Badih Ghattas,2 Pascal Auquier,1 Laurent Boyer1

1Aix-Marseille Univ, School of Medicine, CEReSS - Health Service Research and Quality of Life Center, Marseille, France; 2Mathematics Institute of Marseille, Aix-Marseille University, Marseille, France

Background: The aim of this study was to propose an alternative approach to item response theory (IRT) in the development of computerized adaptive testing (CAT) in quality of life (QoL) for patients with multiple sclerosis (MS). This approach relied on decision regression trees (DRTs). A comparison with IRT was undertaken based on precision and validity properties.
Materials and methods:
DRT- and IRT-based CATs were applied on items from a unidimensional item bank measuring QoL related to mental health in MS. The DRT-based approach consisted of CAT simulations based on a minsplit parameter that defines the minimal size of nodes in a tree. The IRT-based approach consisted of CAT simulations based on a specified level of measurement precision. The best CAT simulation showed the lowest number of items and the best levels of precision. Validity of the CAT was examined using sociodemographic, clinical and QoL data.
Results: CAT simulations were performed using the responses of 1,992 MS patients. The DRT-based CAT algorithm with minsplit = 10 was the most satisfactory model, superior to the best IRT-based CAT algorithm. This CAT administered an average of nine items and showed satisfactory precision indicators (R = 0.98, root mean square error [RMSE] = 0.18). The DRT-based CAT showed convergent validity as its score correlated significantly with other QoL scores and showed satisfactory discriminant validity.
Conclusion:
We presented a new adaptive testing algorithm based on DRT, which has equivalent level of performance to IRT-based approach. The use of DRT is a natural and intuitive way to develop CAT, and this approach may be an alternative to IRT.

Keywords:
computerized adaptive testing, binary decision trees, classification and regression trees, item response theory, quality of life, multiple sclerosis

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