Abstract
Abstract
Background
Patient-Reported Outcomes (PROs) are standardized questionnaires used to measure subjective outcomes such as quality of life in healthcare. They are considered paramount to assess the results of therapeutic interventions. However, because their calibration is relative to internal standards in people’s mind, changes in PRO scores are difficult to interpret.
Knowing the smallest value in the score that the patient perceives as change can help. An estimator linking the answers to a Patient Global Rating of Change (PGRC: a question measuring the overall feeling of change) with change in PRO scores is frequently used to obtain this value. In the last 30 years, a plethora of methods have been used to obtain these estimates, but there is no consensus on the appropriate method and no formal definition of this value.
Methods
We propose a model to explain changes in PRO scores and PGRC answers.
Results
A PGRC measures a construct called the Perceived Change (PC), whose determinants are elicited. Answering a PGRC requires discretizing a continuous PC into a category using threshold values that are random variables. Therefore, the populational value of the Minimal Perceived Change (MPC) is the location parameter value of the threshold on the PC continuum defining the switch from the absence of change to change.
Conclusions
We show how this model can help to hypothesize what are the appropriate methods to estimate the MPC and its potential to be a rigorous theoretical basis for future work on the interpretation of change in PRO scores.
Funder
Agence Nationale de la recherche
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Epidemiology
Reference43 articles.
1. Beaton DE, Boers M, Wells GA. Many faces of the minimal clinically important difference (MCID): a literature review and directions for future research. Curr Opin Rheumatol. 2002;14(2):109–14.
2. Beaujean A. Latent variable modeling using R. A step-by-step guide: Taylor and Francis; 2014.
3. Brozeck, J. L. 2006 How a well-grounded minimal important difference can enhance transparency of labelling claims and improve interpretation of a patient reported outcome measure Health Qual Life Outcomes 7
4. Cohen, J. (2009). Statistical power analysis for the behavioral sciences (2. ed., reprint). Psychology Press
5. Fayers, P. M., & Machin, D. Quality of life : The assessment, analysis, and interpretation of patient-reported outcomes (2nd ed). J. Wiley. 2007
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