Aureliano Paolo Finch1, John Edward Brazier2, Clara Mukuria2. 1. Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK. Electronic address: a.p.finch@vu.nl. 2. Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, UK.
Abstract
BACKGROUND: Generic preference-based measures may miss dimensions important for the health-related quality of life (HRQOL) of patients. When this happens, a possible solution is to add bolt-ons. Finch et al. (Finch AP, Brazier JE, Mukuria C, Bjorner JB. An exploratory study on using principal component analysis and confirmatory factor analysis to identify bolt-on dimensions: the EQ-5D case study. Value Health 2017;10:1362-75) have recently shown that bolt-ons can be systematically identified using factor analysis. Nevertheless, because for each bolt-on option a complete re-evaluation may be required, methods to select between them are needed. OBJECTIVES: To investigate the possibility of selecting bolt-ons using their ability to predict differences in HRQOL. It tests six factors (energy/vitality, satisfaction, relationships, hearing, vision, and speech), and 37 items loading on them, using the EuroQol five-dimensional questionnaire as a case study. METHODS: Data were obtained from the Multi-Instrument Comparison study, an online survey on health and well-being measures carried out in six countries. Two tests were performed. In the first test, linear regressions were fitted to determine whether different bolt-ons helped explain variations in HRQOL as measured by the Health visual analogue scale. The upper anchor (100) of this scale represents excellent physical, mental, and social health, and the lower anchor (0) represents death. Bolt-on relevance was judged comparing the strength, direction, and statistical significance of unadjusted β coefficients. In the second test, linear regressions were fitted to further investigate whether different factors and items helped explain the negative effect of six chronic conditions on HRQOL. A reduction in the coefficients for the chronic condition dummies meant that the factor or item detected the effect. RESULTS: Energy/vitality, relationships, and satisfaction reported substantially larger coefficients than did speech, vision, and hearing. Also, items loading on energy/vitality, relationships, and satisfaction generally presented larger coefficients than did those loading on speech, vision, and hearing. The second test did not detect consistent decrements in the coefficients for chronic conditions when testing factors, but it generally did detect consistent decrements when testing items. CONCLUSIONS: The first test appeared useful for bolt-on selection. Further research is needed before using the second test.
BACKGROUND: Generic preference-based measures may miss dimensions important for the health-related quality of life (HRQOL) of patients. When this happens, a possible solution is to add bolt-ons. Finch et al. (Finch AP, Brazier JE, Mukuria C, Bjorner JB. An exploratory study on using principal component analysis and confirmatory factor analysis to identify bolt-on dimensions: the EQ-5D case study. Value Health 2017;10:1362-75) have recently shown that bolt-ons can be systematically identified using factor analysis. Nevertheless, because for each bolt-on option a complete re-evaluation may be required, methods to select between them are needed. OBJECTIVES: To investigate the possibility of selecting bolt-ons using their ability to predict differences in HRQOL. It tests six factors (energy/vitality, satisfaction, relationships, hearing, vision, and speech), and 37 items loading on them, using the EuroQol five-dimensional questionnaire as a case study. METHODS: Data were obtained from the Multi-Instrument Comparison study, an online survey on health and well-being measures carried out in six countries. Two tests were performed. In the first test, linear regressions were fitted to determine whether different bolt-ons helped explain variations in HRQOL as measured by the Health visual analogue scale. The upper anchor (100) of this scale represents excellent physical, mental, and social health, and the lower anchor (0) represents death. Bolt-on relevance was judged comparing the strength, direction, and statistical significance of unadjusted β coefficients. In the second test, linear regressions were fitted to further investigate whether different factors and items helped explain the negative effect of six chronic conditions on HRQOL. A reduction in the coefficients for the chronic condition dummies meant that the factor or item detected the effect. RESULTS: Energy/vitality, relationships, and satisfaction reported substantially larger coefficients than did speech, vision, and hearing. Also, items loading on energy/vitality, relationships, and satisfaction generally presented larger coefficients than did those loading on speech, vision, and hearing. The second test did not detect consistent decrements in the coefficients for chronic conditions when testing factors, but it generally did detect consistent decrements when testing items. CONCLUSIONS: The first test appeared useful for bolt-on selection. Further research is needed before using the second test.
Authors: Katie Breheny; William Hollingworth; Rebecca Kandiyali; Padraig Dixon; Abi Loose; Pippa Craggs; Mariusz Grzeda; John Sparrow Journal: Qual Life Res Date: 2020-02-20 Impact factor: 4.147