BIOMEDICAL RESEARCH ARCHITECTURE: A METHODOLOGICAL GUIDE TO A PRIORI POWER ANALYSIS

This methodological article focuses on one of the earliest stages of planning biomedical research: a priori statistical power analysis. In the context of the scientific discourse on the problem of result reproducibility, empirical sample size calculation is giving way to rigorous mathematically justified approaches, which is consistent with the principles of evidence-based medicine, the requirements of bioethics, and the objectives of healthcare economics. The text details the probability theory behind hypothesis testing, examining the relationship between Type I and Type II errors. INEXT, the smallest effect size of interest (SESOI) is specified. Practical calculations force researchers to adjust for multiple comparisons and map out subgroup analyses. The text also covers how to handle groups of unequal sizes and what to do about expected patient dropouts. Later in the text, we review several software packages. We focused heavily on platforms with Russian interfaces because local doctors need tools they can easily navigate. We examine the open- source software Jamovi to show exactly how this works. We look at two of its modules to show the exact contrast. jPower targets only the standard parametric t-test. The jYS plugin, by contrast, handles much more complex tasks. It allows researchers to calculate a priori power for the multifactorial and non-parametric models that actually appear in real clinical datasets.

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