Meta-analysis provides evidence-based effect sizes for a cancer-specific quality-of-life questionnaire, the FACT-G.
- Publication Type:
- Journal Article
- Citation:
- J Clin Epidemiol, 2010, 63 (3), pp. 270 - 281
- Issue Date:
- 2010-03
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OBJECTIVE: To compare Cohen's guidelines for small (0.2), medium (0.5), and large (0.8) effect sizes with empirical estimates for a cancer-specific health-related quality-of-life questionnaire (HRQOL), the Functional Assessment of Cancer Therapy - General (FACT-G). METHODS: Seventy-one papers satisfied inclusion criteria for meta-analysis. Blinded to the HRQOL results, three "experts" (with expertise in interpreting the FACT-G questionnaire and managing cancer patients), predicted the relative magnitude of HRQOL mean differences. Size classes (small, medium, large) were defined in terms of relevance to clinical decision making. The experts worked independently and based their predictions on patient characteristics and clinical circumstances. Their judgments were linked with FACT-G results and inverse-variance-weighted mean effect sizes calculated for each size class. RESULTS: At least two experts were perfectly concordant and up to one was discordant by at most one size category for 833 of the mean differences; for these, weighted kappas were generally in the "substantial" range (0.60-0.79). Of these mean differences, 617 were cross-sectional; small, medium, and large mean effect sizes were physical well-being 0.42, 0.87, 1.6; functional well-being 0.37, 0.71, 1.6; emotional well-being 0.32, 0.40, no large differences; and social well-being 0.14, 0.23, no large differences. Two hundred and sixteen longitudinal mean differences yielded small and medium effect sizes: physical well-being 0.26, 0.34; functional well-being 0.14, 0.28; emotional well-being 0.27, 0.23; and social well-being 0.08, 0.01. There was virtually no evidence for large longitudinal effects. CONCLUSION: These results provide specific, evidence-based alternatives to Cohen's generic guidelines, for use in sample-size calculations for the FACT-G and interpretation of the clinical significance of effects measured with FACT-G.
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