Extending basic principles of measurement models to the design and validation of Patient Reported Outcomes
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Commentary
Extending basic principles of measurement models to the design and validation of Patient Reported Outcomes Mark J Atkinson*1,2 and Richard D Lennox3 Address: 1Worldwide Health Outcomes Research, La Jolla Laboratories, Pfizer Inc., San Diego, CA 92121, US, 2Health Services Research Center, USCD School of Medicine, La Jolla, CA 92093, US and 3Psychometric Technologies, Inc., 402 Millstone Drive, Suite A, Hillsborough, NC 27278, US Email: Mark J Atkinson* - [email protected]; Richard D Lennox - [email protected] * Corresponding author
Published: 22 September 2006 Health and Quality of Life Outcomes 2006, 4:65
doi:10.1186/1477-7525-4-65
Received: 13 July 2006 Accepted: 22 September 2006
This article is available from: http://www.hqlo.com/content/4/1/65 © 2006 Atkinson and Lennox; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract A recently published article by the Scientific Advisory Committee of the Medical Outcomes Trust presents guidelines for selecting and evaluating health status and health-related quality of life measures used in health outcomes research. In their article, they propose a number of validation and performance criteria with which to evaluate such self-report measures. We provide an alternate, yet complementary, perspective by extending the types of measurement models which are available to the instrument designer. During psychometric development or selection of a Patient Reported Outcome measure it is necessary to determine which, of the five types of measurement models, the measure is based on; 1) a Multiple Effect Indicator model, 2) a Multiple Cause Indicator model, 3) a Single Item Effect Indicator model, 4) a Single Item Cause Indicator model, or 5) a Mixed Multiple Indicator model. Specification of the measurement model has a major influence on decisions about item and scale design, the appropriate application of statistical validation methods, and the suitability of the resulting measure for a particular use in clinical and population-based outcomes research activities.
Background Over the past two decades, health outcomes researchers have tried to present convincing evidence to regulatory agencies and healthcare planners that Patient Reported Outcomes (PROs) provide a benefit beyond the assessment of clinical outcomes alone. This persistent belief in the added value of patients' ratings of illness and treatment has resulted in a continual refinement of PROs measures for use in clinical settings. Although conceptually and philosophically appealing, widespread acceptance of PROs has generally proved to be a challenge in regulatory and clinical environments, where a high value is placed on biomedical outcomes and where skepticism persists about the meaningfulness of such concepts such
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