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Measuring the Wellbeing of Cancer Patients with Generic and Disease-Specific Instruments
SIMPLE SUMMARY: Patient-reported outcomes play an important role in clinical trials and health economic evaluation. In addition to health-related quality of life, there has been increasing recognition of measuring wider wellbeing that goes beyond health. This study aims to understand the sensitivity...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954597/ https://www.ncbi.nlm.nih.gov/pubmed/36831692 http://dx.doi.org/10.3390/cancers15041351 |
Sumario: | SIMPLE SUMMARY: Patient-reported outcomes play an important role in clinical trials and health economic evaluation. In addition to health-related quality of life, there has been increasing recognition of measuring wider wellbeing that goes beyond health. This study aims to understand the sensitivity and comparability of commonly used preference-based health-related quality of life and subjective wellbeing measures in patients with cancer. This study further explored the life domain importance of cancer patients. The findings from this study shed light on the choice of patient-reported outcome measures in clinical studies as well as the prioritized aspects to improve the overall life satisfaction of cancer patients. ABSTRACT: Different wellbeing measures have been used among cancer patients. This study aimed to first investigate the sensitivity of health state utility (HSU), capability, and subjective wellbeing (SWB) instruments in cancer. A cancer-specific instrument (QLQ-C30) was included and transferred onto the cancer-specific HSU scores. Furthermore, it examined the relative importance of key life domains explaining overall life satisfaction. Data were drawn from the Multi-instrument Comparison survey. Linear regression was used to explore the extent to which the QLQ-C30 sub-scales explain HSU and SWB. Kernel-based Regularized Least Squares (KRLS), a machine learning method, was used to explore the life domain importance of cancer patients. As expected, the QLQ-C30 sub-scales explained the vast majority of the variance in its derived cancer-specific HSU (R(2) = 0.96), followed by generic HSU instruments (R(2) of 0.65–0.73) and SWB and capability instruments (R(2) of 0.33–0.48). The cancer-specific measure was more closely correlated with generic HSU than SWB measures, owing to the construction of these instruments. In addition to health, life achievements, relationships, the standard of living, and future security all play an important role in explaining the overall life satisfaction of cancer patients. |
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