Abstract
Purpose
To use published literature to estimate large, medium, and small differences in quality of life (QOL) data from the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30).
Methods
An innovative method combining systematic review of published studies, expert opinions, and meta-analysis was used to estimate large, medium, and small differences for QLQ-C30 scores. Published mean data were identified from the literature. Differences (contrasts) between groups (eg, between treatment groups, age groups, and performance status groups) were reviewed by 34 experts in QOL measurement and cancer treatment. The experts, blinded to actual QOL results, were asked to predict these differences. A large difference was defined as one representing unequivocal clinical relevance. A medium difference was defined as likely to be clinically relevant but to a lesser extent. A small difference was one believed to be subtle but nevertheless clinically relevant. A trivial difference was used to describe circumstances unlikely to have any clinical relevance. Actual QOL results were combined using meta-analytic techniques to estimate differences corresponding to small, medium, or large effects.
Results
Nine hundred eleven articles were identified, leading to 152 relevant articles (2,217 contrasts) being reviewed by at least two experts. Resulting estimates from the meta-analysis varied depending on the subscale. Thus, the recommended minimum to detect medium differences ranges from 9 (cognitive functioning) to 19 points (role functioning).
Conclusion
Guidelines for the size of effects are provided for the QLQ-C30 subscales. These guidelines can be used for sample size calculations for clinical trials and can also be used to aid interpretation of differences in QLQ-C30 scores
To use published literature to estimate large, medium, and small differences in quality of life (QOL) data from the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30).
Methods
An innovative method combining systematic review of published studies, expert opinions, and meta-analysis was used to estimate large, medium, and small differences for QLQ-C30 scores. Published mean data were identified from the literature. Differences (contrasts) between groups (eg, between treatment groups, age groups, and performance status groups) were reviewed by 34 experts in QOL measurement and cancer treatment. The experts, blinded to actual QOL results, were asked to predict these differences. A large difference was defined as one representing unequivocal clinical relevance. A medium difference was defined as likely to be clinically relevant but to a lesser extent. A small difference was one believed to be subtle but nevertheless clinically relevant. A trivial difference was used to describe circumstances unlikely to have any clinical relevance. Actual QOL results were combined using meta-analytic techniques to estimate differences corresponding to small, medium, or large effects.
Results
Nine hundred eleven articles were identified, leading to 152 relevant articles (2,217 contrasts) being reviewed by at least two experts. Resulting estimates from the meta-analysis varied depending on the subscale. Thus, the recommended minimum to detect medium differences ranges from 9 (cognitive functioning) to 19 points (role functioning).
Conclusion
Guidelines for the size of effects are provided for the QLQ-C30 subscales. These guidelines can be used for sample size calculations for clinical trials and can also be used to aid interpretation of differences in QLQ-C30 scores
| Original language | English |
|---|---|
| Pages (from-to) | 89-96 |
| Number of pages | 8 |
| Journal | Journal of Clinical Oncology |
| Volume | 29 |
| Issue number | 1 |
| Early online date | 22 Nov 2010 |
| DOIs | |
| Publication status | Published - 1 Jan 2011 |
Bibliographical note
AcknowledgmentWe thank Cancer Research United Kingdom for funding for the Evidence-Based Interpretation Guidelines (EBIG) project. K.C. would like to thank the UICC International Cancer Technology Transfer Fellowship program; G.V. is grateful to Cancer Research United Kingdom for financial support. We thank Jane Blazeby, Penny Wright, and Jane Maher for advice regarding the patient interviews.
