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Breaking free of sample size dogma to perform innovative translational research
Peter Bacchetti1, Steven G Deeks, Joseph M McCune
1Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143, USA. peter@biostat.ucsf.edu
Abstract:
Innovative clinical and translational research is often delayed or prevented by reviewers' expectations that any study performed in humans must be shown in advance to have high statistical power. This supposed requirement is not justifiable and is contradicted by the reality that increasing sample size produces diminishing marginal returns. Studies of new ideas often must start small (sometimes even with an n of 1) because of cost and feasibility concerns, and recent statistical work shows that small sample sizes for such research can produce more projected scientific value per dollar spent than larger sample sizes. Renouncing false dogma about sample size would remove a serious barrier to innovation and translation.
Insights
Reviewers often demand high statistical power, delaying innovative research. Small sample sizes can be more cost-effective for early-stage studies, offering greater scientific value per dollar spent.
Area of Science:
- Clinical research
- Translational science
- Biostatistics
Background:
- Reviewers frequently require high statistical power for human studies, hindering innovation.
- This expectation is not statistically justified and ignores diminishing returns with increasing sample size.
Purpose of the Study:
- To challenge the dogma of mandatory high statistical power in early-stage research.
- To advocate for the acceptance of small sample sizes in innovative clinical and translational studies.
Main Methods:
- The study critiques current review practices regarding statistical power requirements.
- It references recent statistical work on the cost-effectiveness of small sample sizes.
Main Results:
- High statistical power is not always necessary or cost-effective for initial studies.
- Small sample size research can yield higher projected scientific value per dollar.
Conclusions:
- Renouncing the false dogma of high statistical power requirements would accelerate innovation.
- Accepting smaller sample sizes removes a significant barrier in clinical and translational research.
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