PURPOSE: The purpose of this analysis was to determine the unique contribution of household income to the variance explained in psychological well-being (PWB) among a sample of colorectal cancer (CRC) survivors. METHODS: This study is a secondary analysis of data collected as part of the Health-Related Quality of Life in Long-Term Colorectal Cancer Survivors Study, which included CRC survivors with (cases) and without (controls) ostomies. The dataset included socio-demographic, health status, and health-related quality of life (HRQOL) information. HRQOL was assessed with the modified City of Hope Quality of Life (mCOH-QOL)-Ostomy questionnaire and SF-36v2. To assess the relationship between income and PWB, a hierarchical linear regression model was constructed combining data from both cases and controls. RESULTS: After accounting for the proportion of variance in PWB explained by the other independent variables in the model, the additional variance explained by income was significant (R (2) increased from 0.228 to 0.250; P = 0.006). CONCLUSIONS: Although the study design does not allow causal inference, these results demonstrate a significant relationship between income and PWB in CRC survivors. The findings suggest that for non-randomized group comparisons of HRQOL, income should, at the very least, be included as a control variable in the analysis.
PURPOSE: The purpose of this analysis was to determine the unique contribution of household income to the variance explained in psychological well-being (PWB) among a sample of colorectal cancer (CRC) survivors. METHODS: This study is a secondary analysis of data collected as part of the Health-Related Quality of Life in Long-Term Colorectal Cancer Survivors Study, which included CRC survivors with (cases) and without (controls) ostomies. The dataset included socio-demographic, health status, and health-related quality of life (HRQOL) information. HRQOL was assessed with the modified City of Hope Quality of Life (mCOH-QOL)-Ostomy questionnaire and SF-36v2. To assess the relationship between income and PWB, a hierarchical linear regression model was constructed combining data from both cases and controls. RESULTS: After accounting for the proportion of variance in PWB explained by the other independent variables in the model, the additional variance explained by income was significant (R (2) increased from 0.228 to 0.250; P = 0.006). CONCLUSIONS: Although the study design does not allow causal inference, these results demonstrate a significant relationship between income and PWB in CRC survivors. The findings suggest that for non-randomized group comparisons of HRQOL, income should, at the very least, be included as a control variable in the analysis.
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