Evidence-based effect size estimation: an illustration using the case of acupuncture for cancer-related fatigue.
BACKGROUND
Estimating a realistic effect size is an important issue in the planning of clinical studies of complementary and alternative medicine therapies. When a minimally important difference is not available, researchers may estimate effect size using the published literature. This evidence-based effect size estimation may be used to produce a range of empirically-informed effect size and consequent sample size estimates. We provide an illustration of deriving plausible effect size ranges for a study of acupuncture in the relief of post-chemotherapy fatigue in breast cancer patients.


METHODS
A PubMed search identified three uncontrolled studies reporting the effect of acupuncture in relieving fatigue. A separate search identified five randomized controlled trials (RCTs) with a wait-list control of breast cancer patients receiving standard care that reported data on fatigue. We use these published data to produce best, average, and worst-case effect size estimates and related sample size estimates for a trial of acupuncture in the relief of cancer-related fatigue relative to a wait-list control receiving standard care.


RESULTS
Use of evidence-based effect size estimation to calculate sample size requirements for a study of acupuncture in relieving fatigue in breast cancer survivors relative to a wait-list control receiving standard care suggests that an adequately-powered phase III randomized controlled trial comprised of two arms would require at least 101 subjects (52 per arm) if a strong effect is assumed for acupuncture and 235 (118 per arm) if a moderate effect is assumed.


CONCLUSIONS
Evidence-based effect size estimation helps justify assumptions in light of empirical evidence and can lead to more realistic sample size calculations, an outcome that would be of great benefit for the field of complementary and alternative medicine.
Johnston
Michael F
MF
Center for East-West Medicine, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA. johnston@ucla.edu
Hays
Ron D
RD
Hui
Ka-Kit
KK
D015670:Acupuncture Therapy; D000704:Analysis of Variance; D001943:Breast Neoplasms / Q000150:complications; D002986:Clinical Trials as Topic; D000529:Complementary Therapies; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine / Q000379:methods*; D005221:Fatigue / Q000209:etiology / Q000628:therapy*; D006801:Humans; D012044:Regression Analysis; D012107:Research Design / Q000592:standards*; D018401:Sample Size