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BMC Medical Research Methodology

, 16:142

Data analysis, statistics and modelling


BackgroundRecently, a growing number of Item-Response Theory IRT models has been published, which allow estimation of a common latent variable from data derived by different Patient Reported Outcomes PROs. When using data from different PROs, direct estimation of the latent variable has some advantages over the use of sum score conversion tables. It requires substantial proficiency in the field of psychometrics to fit such models using contemporary IRT software. We developed a web application http:-www.common-metrics.org, which allows estimation of latent variable scores more easily using IRT models calibrating different measures on instrument independent scales.

ResultsCurrently, the application allows estimation using six different IRT models for Depression, Anxiety, and Physical Function. Based on published item parameters, users of the application can directly estimate latent trait estimates using expected a posteriori EAP for sum scores as well as for specific response patterns, Bayes modal MAP, Weighted likelihood estimation WLE and Maximum likelihood ML methods and under three different prior distributions. The obtained estimates can be downloaded and analyzed using standard statistical software.

ConclusionsThis application enhances the usability of IRT modeling for researchers by allowing comparison of the latent trait estimates over different PROs, such as the Patient Health Questionnaire Depression PHQ-9 and Anxiety GAD-7 scales, the Center of Epidemiologic Studies Depression Scale CES-D, the Beck Depression Inventory BDI, PROMIS Anxiety and Depression Short Forms and others. Advantages of this approach include comparability of data derived with different measures and tolerance against missing values. The validity of the underlying models needs to be investigated in the future.

KeywordsItem-Response Theory Measurement Patient Reported Outcomes Depression Anxiety Physical function  Download fulltext PDF

Autor: H. Felix Fischer - Matthias Rose

Fuente: https://link.springer.com/

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