Dataset Information

Two parallel short forms to measure disease- and treatment-associated knowledge in rheumatoid arthritis: application of item response theory.

ABSTRACT:

Objective

The aim was to develop two disease- and treatment-related knowledge about RA (DataK-RA) short forms using item response theory-based linear optimal test design.

Methods

We used the open source Excel add-in solver to program a linear optimization algorithm to develop two short forms from the DataK-RA item bank. The algorithm was instructed to optimize precision (i.e. reliability) of the scores for both short forms, subject to a number of constraints that served to ensure that each short form would include unique items and that the short forms would have similar psychometric properties. Agreement among item response theory scores obtained from the different short forms was assessed using the Bland-Altman method and Student's paired t-test. Construct validity and relative efficiency of the short forms was evaluated by relating the score to age, sex and educational attainment.

Results

Two short forms were derived from the DataK-RA item bank that satisfied all content constraints. Both short forms included 15 unique items and yielded reliable scores (r > 0.70), with low ceiling and floor effects. The short forms yielded statistically indistinguishable mean scores according to Student's paired t-test and Bland-Altman analysis. Scores on short forms 1 and 2 were associated with age, sex and educational attainment to a similar extent.

Conclusion

In this study, we developed two DataK-RA short forms with unique items, yet similar psychometric properties, that can be used to assess patients pre- and post-test interventions aimed at improving disease-related knowledge in RA patients.

SUBMITTER: Spijk-de Jonge MJ

PROVIDER: S-EPMC7368341 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Two parallel short forms to measure disease- and treatment-associated knowledge in rheumatoid arthritis: application of item response theory.

Spijk-de Jonge Marieke J MJ Oude Voshaar Martijn A H MAH Renskers Lisanne L Huis Anita M P AMP van de Laar Mart A F J MAFJ Hulscher Marlies E J L MEJL van Riel Piet L C M PLCM

Rheumatology advances in practice 20200617 2

<h4>Objective</h4>The aim was to develop two disease- and treatment-related knowledge about RA (DataK-RA) short forms using item response theory-based linear optimal test design.<h4>Methods</h4>We used the open source Excel add-in solver to program a linear optimization algorithm to develop two short forms from the DataK-RA item bank. The algorithm was instructed to optimize precision (i.e. reliability) of the scores for both short forms, subject to a number of constraints that served to ensure ...[more]

PMID: 32704614

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Project description:ObjectivesSurveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children.MethodsThis cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations.ResultsWe developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses.ConclusionsThe combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.

Dataset Information

Two parallel short forms to measure disease- and treatment-associated knowledge in rheumatoid arthritis: application of item response theory.

Objective

Methods

Results

Conclusion

Publications

Two parallel short forms to measure disease- and treatment-associated knowledge in rheumatoid arthritis: application of item response theory.

Similar Datasets

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