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

TL;DR

This paper discusses methods for calculating statistical power and sample size in in vivo biomedical research, emphasizing practical approaches for repeated measures ANOVA to improve study design and ethical compliance.

Contribution

It introduces a practical framework for power and sample size calculation in repeated measures ANOVA using G*Power, making it accessible for experimentalists.

Findings

Demonstrates how to perform a priori power analysis with G*Power

Provides examples of repeated measures ANOVA for sample size estimation

Aims to simplify power analysis for biomedical researchers

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.