Effectiveness research and implications for study design: sample size and statistical power.

Abstract

Most clinical trials have started to incorporate more broadly defined outcome measures, such as health-related quality of life, to complement clinical status measures as well as direct costs and cost-effectiveness analyses. Contrasting a broad range of outcome and cost measures, we analyze the implications for sample sizes and study design using data from prior mental health and primary care studies that span a wide range of practice settings, patient populations, and geographic areas. While meaningful clinical symptomatic differences are often detectable with sample sizes of well under 100 per cell, detecting even large changes in health-related quality of life generally requires several hundred observations per cell. Reasonable precision in cost estimates usually requires sample sizes in the thousands. Very few clinical trials or observational effectiveness studies that incorporate quality of life or cost measures have such sample sizes, resulting in many (unreported) null findings and, due to publication biases favoring significant results, scientific publications that exaggerate true effects. It raises issues for the general direction of clinical trials and effectiveness studies, as well as for how cost and health-related quality of life results based on small studies should be dealt with in publications.