Harrison, Conrad J., Sidey-Gibbons, Chris J., Klassen, Anne F., Wong Riff, Karen W. Y., Furniss, Dominic, Swan, Marc C. and Rodrigues, Jeremy N. (2021) Recursive partitioning vs computerized adaptive testing to reduce the burden of health assessments in cleft lip and/or palate : comparative simulation study. Journal of Medical Internet Research, 23 (7). e26412. doi:10.2196/26412 ISSN 1438-8871.

Preview

PDF WRAP-Recursive-partitioning-vs-computerized-adaptive-testing-Rodrigues-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (334Kb) | Preview

Request Changes to record.

Abstract

Background:
Computerized adaptive testing (CAT) has been shown to deliver short, accurate, and personalized versions of the CLEFT-Q patient-reported outcome measure for children and young adults born with a cleft lip and/or palate. Decision trees may integrate clinician-reported data (eg, age, gender, cleft type, and planned treatments) to make these assessments even shorter and more accurate.

Objective:
We aimed to create decision tree models incorporating clinician-reported data into adaptive CLEFT-Q assessments and compare their accuracy to traditional CAT models.

Methods:
We used relevant clinician-reported data and patient-reported item responses from the CLEFT-Q field test to train and test decision tree models using recursive partitioning. We compared the prediction accuracy of decision trees to CAT assessments of similar length. Participant scores from the full-length questionnaire were used as ground truth. Accuracy was assessed through Pearson’s correlation coefficient of predicted and ground truth scores, mean absolute error, root mean squared error, and a two-tailed Wilcoxon signed-rank test comparing squared error.

Results:
Decision trees demonstrated poorer accuracy than CAT comparators and generally made data splits based on item responses rather than clinician-reported data.

Conclusions:
When predicting CLEFT-Q scores, individual item responses are generally more informative than clinician-reported data. Decision trees that make binary splits are at risk of underfitting polytomous patient-reported outcome measure data and demonstrated poorer performance than CATs in this study.

Item Type:	Journal Article
Subjects:	R Medicine > R Medicine (General) R Medicine > RD Surgery
Divisions:	Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Clinical Trials Unit Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH):	Cleft lip, Cleft palate, Outcome assessment (Medical care), Computer adaptive testing, Machine learning, Decision trees
Journal or Publication Title:	Journal of Medical Internet Research
Publisher:	Journal of Medical Internet Research
ISSN:	1438-8871
Official Date:	30 July 2021
Dates:	Date Event 30 July 2021 Published 24 May 2021 Accepted 10 December 2020 Submitted
Volume:	23
Number:	7
Number of Pages:	8
Article Number:	e26412
DOI:	10.2196/26412
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	19 April 2022
Date of first compliant Open Access:	21 April 2022
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID NIHR300684 [NIHR] National Institute for Health Research http://dx.doi.org/10.13039/501100000272 PDF-2017-10-075 [NIHR] National Institute for Health Research http://dx.doi.org/10.13039/501100000272 UNSPECIFIED NIHR Oxford Biomedical Research Centre http://dx.doi.org/10.13039/501100013373 FRN-130515 Canadian Institutes of Health Research http://dx.doi.org/10.13039/501100000024
Is Part Of:	1

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads