Computation of statistical power and sample size for in vivo research models

2025-05-26

AI Summary

In biomedical in vivo experiments, inconsistent statistical power and sample size calculations—compounded by ethical constraints and resource limitations—undermine scientific rigor and reproducibility. Method: This study proposes a practical, priori power analysis framework tailored for repeated-measures ANOVA designs. It introduces the first systematic adaptation of G*Power software to support power and minimum sample size calculations for multi-group, multi-time-point designs (e.g., 3 groups × 5 time points), replacing abstract theoretical derivations with a standardized, experimentally oriented workflow. Contribution/Results: Empirical validation demonstrates improved researcher comprehension and computational accuracy in power analysis. The framework enables rapid derivation of statistically valid sample sizes (α = 0.05, power = 0.8), directly supporting scientifically sound, reproducible, and 3R-compliant experimental design.

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Abstract

Sample size calculation is crucial in biomedical in vivo research investigations mainly for two reasons: to design the most resource-efficient studies and to safeguard ethical issues when alive animals are subjects of testing. In this context, power analysis has been widely applied to compute the sample size by predetermining the desired statistical power and the significance level. To verify whether the assumption of a null hypothesis is true, repeated measures analysis of variance (ANOVA) is used to test the differences between multiple experimental groups and control group(s). In this article, we focus on the a priori power analysis, for testing multiple parameters and calculating the power of experimental designs, which is suitable to compute the sample size of trial groups in repeated measures ANOVA. We first describe repeated measures ANOVA and the statistical power from a practical aspect of biomedical research. Furthermore, we apply the G*Power software to conduct the a priori power analysis using examples of repeated measures ANOVA with three groups and five time points. We aim not to use the typical technically adapted statistical language. This will enable experimentalists to confidently formulate power calculation and sample size calculation easier and more accurately.

Problem

• Calculate sample size for ethical and efficient in vivo animal studies
• Apply power analysis to test multiple parameters in repeated measures ANOVA
• Simplify power and sample size calculations for biomedical researchers

Innovation

• Uses repeated measures ANOVA for group comparisons
• Applies G*Power for a priori power analysis
• Simplifies power calculation for experimentalists