BACKGROUND: Little attention has been paid to selecting and developing health-related quality of life (HRQOL) measurement tools for young adult survivors of childhood cancer (YASCC). The primary purpose of this study was to develop a HRQOL tool for YASCC based on three legacy instruments. METHODS: Data collected from 151 YASCC were analyzed. HRQOL was measured using the Medical Outcomes Study SF-36, Quality of Life in Adult Cancer Survivors, and Quality of Life-Cancer Survivor. We used the following stages to develop our HRQOL tool: mapping items from three instruments into a common HRQOL construct, checking dimensionality using confirmatory factor analyses (CFA), and equating items using Rasch modeling. RESULTS: We assigned 123 items to a HRQOL construct comprised of six generic and eight survivor-specific domains. CFA retained 107 items that meet the assumptions of unidimensionality and local independence. Rasch analysis retained 68 items that satisfied the indices of information-weighted/outlier-sensitive fit statistic mean square. However, items in most domains possess relatively easy measurement properties, whereas YASCC's underlying HRQOL was on the middle to higher levels. CONCLUSIONS: Psychometric properties of the established tool for measuring HRQOL of YASCC were not satisfied. Future studies need to refine this tool, especially adding more challenging items.
BACKGROUND: Little attention has been paid to selecting and developing health-related quality of life (HRQOL) measurement tools for young adult survivors of childhood cancer (YASCC). The primary purpose of this study was to develop a HRQOL tool for YASCC based on three legacy instruments. METHODS: Data collected from 151 YASCC were analyzed. HRQOL was measured using the Medical Outcomes Study SF-36, Quality of Life in Adult Cancer Survivors, and Quality of Life-Cancer Survivor. We used the following stages to develop our HRQOL tool: mapping items from three instruments into a common HRQOL construct, checking dimensionality using confirmatory factor analyses (CFA), and equating items using Rasch modeling. RESULTS: We assigned 123 items to a HRQOL construct comprised of six generic and eight survivor-specific domains. CFA retained 107 items that meet the assumptions of unidimensionality and local independence. Rasch analysis retained 68 items that satisfied the indices of information-weighted/outlier-sensitive fit statistic mean square. However, items in most domains possess relatively easy measurement properties, whereas YASCC's underlying HRQOL was on the middle to higher levels. CONCLUSIONS: Psychometric properties of the established tool for measuring HRQOL of YASCC were not satisfied. Future studies need to refine this tool, especially adding more challenging items.
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