PURPOSE: This study aims to compare the Functional Assessment of Cancer Therapy-General (FACT-G) quality of life (QOL) scores of patient groups with varying clinical and sociodemographic features in the early stage cancer population. METHODS: A literature search was conducted on both the Embase and OvidSP platforms. Weighted analysis of variance (ANOVA) was performed for binary predictors and weighted linear regression analysis was conducted for continuous predictors. RESULTS: Six binary features predicted at least one domain of QOL: primary cancer site (homogeneous versus heterogeneous), total per capita healthcare expenditures, mean age, previous chemotherapy, radiotherapy, and previous hormonal therapy. Two continuous factors had predictive value with respect to QOL: completion of postsecondary education and marital status. CONCLUSION: Although there are limitations of the current study, similar correlations to our own have been previously described between QOL and healthcare expenditures, mean age and education. Currently, the literature conflicts in its analysis of previous radiotherapy and chemotherapy as predictors of QOL. No published evidence exists describing the presently found relationships in primary cancer site, marital status and hormonal therapy. Future work may focus on determining cause and effect relationships between these predictors and QOL.
PURPOSE: This study aims to compare the Functional Assessment of Cancer Therapy-General (FACT-G) quality of life (QOL) scores of patient groups with varying clinical and sociodemographic features in the early stage cancer population. METHODS: A literature search was conducted on both the Embase and OvidSP platforms. Weighted analysis of variance (ANOVA) was performed for binary predictors and weighted linear regression analysis was conducted for continuous predictors. RESULTS: Six binary features predicted at least one domain of QOL: primary cancer site (homogeneous versus heterogeneous), total per capita healthcare expenditures, mean age, previous chemotherapy, radiotherapy, and previous hormonal therapy. Two continuous factors had predictive value with respect to QOL: completion of postsecondary education and marital status. CONCLUSION: Although there are limitations of the current study, similar correlations to our own have been previously described between QOL and healthcare expenditures, mean age and education. Currently, the literature conflicts in its analysis of previous radiotherapy and chemotherapy as predictors of QOL. No published evidence exists describing the presently found relationships in primary cancer site, marital status and hormonal therapy. Future work may focus on determining cause and effect relationships between these predictors and QOL.
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