Literature DB >> 30556774

Measurement of patient-reported outcomes. 1: The search for the Holy Grail.

Stephen P McKenna1,2, Alice Heaney1, Jeanette Wilburn1, A Jackson Stenner3.   

Abstract

Patient-reported outcome measures (PROMs) are used to collect information directly from patients. They may cover several different types of outcomes ranging from symptoms, functioning, utility, satisfaction, through to quality-of-life (QoL). They generally consist of self-completed questionnaires that can be administered by means of hard copies or in a range of electronic formats. PROMs vary considerably in terms of the constructs they assess, the care with which they are developed, and their scientific quality. However, none of the PROMs available approach the quality of measurement achieved by measures/instruments used in physics. PROs are examples of latent variables. These are not directly observable, but can be inferred from, for example, responses to a questionnaire. The only measure of a latent variable that approaches the quality of measurement achieved by the physical sciences is the Lexile Framework for Reading. This framework is based on a construct theory that grew out of an analysis of several available reading measures. A specification equation was generated that was able to link the construct theory to scores obtained with the Lexile measure. A fundamental requirement of this quality of measurement is that the data collected with the model fit Rasch Measurement Theory (RMT). It is argued that PROM developers should aspire to match this level of measurement sophistication if their instruments are to provide valid insights into the impacts of disease and its treatment.

Entities:  

Keywords:  C00; Construct validity; I10; Rasch Measurement Theory; objective measurement; patient reported outcome; questionnaire; response model; unidimensionality

Mesh:

Year:  2019        PMID: 30556774     DOI: 10.1080/13696998.2018.1560303

Source DB:  PubMed          Journal:  J Med Econ        ISSN: 1369-6998            Impact factor:   2.448


  11 in total

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10.  Measurement, modeling and QALYs.

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