Studies with low statistical power increase the likelihood that a statistically significant finding represents a false positive result. We conducted a review of meta-analyses of studies investigating the association of biological, environmental or cognitive parameters with neurological, psychiatric and somatic diseases, excluding treatment studies, in order to estimate the average statistical power across these domains. Taking the effect size indicated by a meta-analysis as the best estimate of the likely true effect size, and assuming a threshold for declaring statistical significance of 5%, we found that approximately 50% of studies have statistical power in the 0-10% or 11-20% range, well below the minimum of 80% that is often considered conventional. Studies with low statistical power appear to be common in the biomedical sciences, at least in the specific subject areas captured by our search strategy. However, we also observe evidence that this depends in part on research methodology, with candidate gene studies showing very low average power and studies using cognitive/behavioural measures showing high average power. This warrants further investigation.
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http://dx.doi.org/10.1098/rsos.160254 | DOI Listing |
PLoS One
January 2025
Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, Kingdom of Saudi Arabia.
Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded by more sophisticated approaches.
View Article and Find Full Text PDFBiometrics
January 2025
Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, United States.
Distributed lag models (DLMs) estimate the health effects of exposure over multiple time lags prior to the outcome and are widely used in time series studies. Applying DLMs to retrospective cohort studies is challenging due to inconsistent lengths of exposure history across participants, which is common when using electronic health record databases. A standard approach is to define subcohorts of individuals with some minimum exposure history, but this limits power and may amplify selection bias.
View Article and Find Full Text PDFStat Med
February 2025
Villanova University, Villanova, Pennsylvania, USA.
We study the problem of testing multiple secondary endpoints conditional on a primary endpoint being significant in a two-stage group sequential procedure, focusing on two secondary endpoints. This extends our previous work with one secondary endpoint. The test for the secondary null hypotheses is a closed procedure.
View Article and Find Full Text PDFStat Med
February 2025
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY.
Clinical trials are often designed based on limited information about effect sizes and precision parameters with risks of underpowered studies. This is more problematic for SMARTs where strategy effects are based on sequences of treatments. Sample size adjustment offers flexibility through re-estimating sample size during the trial to ensure adequate power at the final analysis.
View Article and Find Full Text PDFStat Med
February 2025
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
In bioequivalence design, power analyses dictate how much data must be collected to detect the absence of clinically important effects. Power is computed as a tail probability in the sampling distribution of the pertinent test statistics. When these test statistics cannot be constructed from pivotal quantities, their sampling distributions are approximated via repetitive, time-intensive computer simulation.
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