Comparison of CTT and Rasch-based approaches for the analysis of longitudinal Patient Reported Outcomes

Authors

  • Myriam Blanchin,

    Corresponding author
    1. EA 4275 ‘Biostatistics, Clinical Research and Subjective Measures in Health Sciences’, Faculty of Pharmaceutical Sciences, University of Nantes, Nantes, France
    • EA 4275 ‘Biostatistics, Clinical Research and Subjective Measures in Health Sciences’, Faculté de Pharmacie—Université de Nantes, 1, rue Gaston Veil—44035 Nantes Cedex 1, France
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  • Jean-Benoit Hardouin,

    1. EA 4275 ‘Biostatistics, Clinical Research and Subjective Measures in Health Sciences’, Faculty of Pharmaceutical Sciences, University of Nantes, Nantes, France
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  • Tanguy Le Neel,

    1. EA 4275 ‘Biostatistics, Clinical Research and Subjective Measures in Health Sciences’, Faculty of Pharmaceutical Sciences, University of Nantes, Nantes, France
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  • Gildas Kubis,

    1. EA 4275 ‘Biostatistics, Clinical Research and Subjective Measures in Health Sciences’, Faculty of Pharmaceutical Sciences, University of Nantes, Nantes, France
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  • Claire Blanchard,

    1. Department of Digestive and Endocrine Surgery/Institut des Maladies de l'Appareil Digestif, CHU Nantes, Faculty of Medicine, University of Nantes, France
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  • Eric Mirallié,

    1. Department of Digestive and Endocrine Surgery/Institut des Maladies de l'Appareil Digestif, CHU Nantes, Faculty of Medicine, University of Nantes, France
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  • Véronique Sébille

    1. EA 4275 ‘Biostatistics, Clinical Research and Subjective Measures in Health Sciences’, Faculty of Pharmaceutical Sciences, University of Nantes, Nantes, France
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Abstract

Health sciences frequently deal with Patient Reported Outcomes (PRO) data for the evaluation of concepts, in particular health-related quality of life, which cannot be directly measured and are often called latent variables. Two approaches are commonly used for the analysis of such data: Classical Test Theory (CTT) and Item Response Theory (IRT). Longitudinal data are often collected to analyze the evolution of an outcome over time. The most adequate strategy to analyze longitudinal latent variables, which can be either based on CTT or IRT models, remains to be identified. This strategy must take into account the latent characteristic of what PROs are intended to measure as well as the specificity of longitudinal designs. A simple and widely used IRT model is the Rasch model. The purpose of our study was to compare CTT and Rasch-based approaches to analyze longitudinal PRO data regarding type I error, power, and time effect estimation bias. Four methods were compared: the Score and Mixed models (SM) method based on the CTT approach, the Rasch and Mixed models (RM), the Plausible Values (PV), and the Longitudinal Rasch model (LRM) methods all based on the Rasch model. All methods have shown comparable results in terms of type I error, all close to 5 per cent. LRM and SM methods presented comparable power and unbiased time effect estimations, whereas RM and PV methods showed low power and biased time effect estimations. This suggests that RM and PV methods should be avoided to analyze longitudinal latent variables. Copyright © 2010 John Wiley & Sons, Ltd.

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