Quality of Life Research

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Experts discussed ways of implementing quality of life research in health care.
Image Caption: 
Conference attendee and graduate student Teresa Tsui (pictured) works in the laboratory of Dr. Murray Krahn, Toronto General Research Institute part of University Health Network.

Conference: International Society for Quality of Life Research, October 18-21, 2017, Philadelphia, Pennsylvania, USA.

Conference Highlight: The ISOQOL conference brought together international researchers to discuss theory, methodology and implementation of quality of life research applied to health care policy and practice.

Conference Summary: The International Society for Quality of Life (ISOQOL) conference was a great opportunity for me to learn about new developments in health-related quality of life research.

Health-related quality of life (HRQoL) as defined by the World Health Organization comprises physical health, psychological state, level of independence, social relationships and their relationship to salient features of their environment (1). HRQoL can be measured using psychometric or preference-based questionnaires (instruments). Psychometric approaches are intended to differentiate between known groups, measure changes in health status, and predict future health outcomes (2). Preference-based approaches account for a patient’s value of health attributes. For example, two patients undergoing hormone therapy treatment for breast cancer may experience similar menopausal symptoms but differ greatly in how they value their health state (3). Understanding patient preferences for HRQoL outcomes is important for health economic evaluations, which are often used to aid in decisions about resource allocation (4). Several of the latest HRQoL methodologies were showcased at the conference, including new mapping algorithms and novel instruments.

Mapping methodologies are popular because the UK’s National Institute for Health and Care Excellence now accepts mapping algorithms to mathematically convert psychometric scores to preferences (5). These preference scores are critical to determining the economic benefits of novel interventions. At the ISOQOL conference, I learned that the cancer-specific psychometric instrument EORTC-QLQ-C30, was mapped to the generic preference-based instruments EQ-5D and SF-6D. This allows clinical trial outcomes that use the QLQ-C30 psychometric instrument to be converted to preferences, to inform economic analyses. I also learned that there are limitations to mapping because of lack of congruence in disease-specific content between the disease-specific instrument and the generic preference-based instrument, and ceiling and floor effects of EQ-5D and SF-6D, respectively. Ceiling effects occur when the instrument is unable to detect changes in high scores ie, when people are generally well. Floor effects refer to the instrument being unable to detect changes in low scores ie, when people are unwell.

To address some of the limitations associated with mapping, novel utility instruments are being developed from existing psychometric instruments. Existing psychometric instruments are also being evaluated using item-response theory (IRT) methods. IRT methods are a collection of statistical modeling techniques that measure inter-individual variation in health status, including HRQoL (6). Different schools of thought related to the evaluation of existing psychometric instruments were represented at ISOQOL – mostly from Europeans who use the Rasch model, and researchers from the U.S. who use the two parameter item response theory (IRT) model. I found it intriguing that these researchers did not use Rasch and IRT methods together. I also found it intriguing that even though there are philosophical differences underlying the Rasch and IRT models, mathematically, the models are more similar than different.

Overall, presenting my research and attending the ISOQOL conference allowed me to envision ways to improve the methodological rigor of creating a novel preference-based instrument in breast cancer and identify more applications of my novel instrument.

References:

1. The World Health Organization Quality of Life Assessment (WHOQOL): development and general psychometric properties. Soc Sci Med. 1998;46(12):1569-85.

2. Revicki DA, Kaplan RM. Relationship between psychometric and utility-based approaches to the measurement of health-related quality of life. Qual Life Res. 1993;2(6):477-87.

3. Regan MM, Francis PA, Pagani O, Fleming GF, Walley BA, Viale G, et al. Absolute Benefit of Adjuvant Endocrine Therapies for Premenopausal Women With Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Early Breast Cancer: TEXT and SOFT Trials. J Clin Oncol. 2016;34(19):2221-31.

4. Torrance GW. Measurement of health state utilities for economic appraisal. J Health Econ. 1986;5(1):1-30.

5. Wailoo AJ, Hernandez-Alava M, Manca A, Mejia A, Ray J, Crawford B, et al. Mapping to Estimate Health-State Utility from Non-Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report. Value Health. 2017;20(1):18-27.

6. Edelen MO, Reeve BB. Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Qual Life Res. 2007;16 Suppl 1:5-18.