Sample size calculation for cross-sectional studies

The cross-sectional study design is widely prevalent in Russian medical literature. However, a significant number of these studies neglect to calculate the sample size during the planning phase, and the analysis often relies solely on basic bivariate statistics. This compromises the validity of the findings and increases the risk of drawing inaccurate conclusions. The scientific rigor of a study depends on a quality of planning, a clear problem statement, and precise formulation of statistical hypotheses, which are then tested using the most appropriate analytical methods. At the core of this process lies the determination of the appropriate sample size. The primary objective of this article is to provide a comprehensive, step-by-step guide for the sample size calculation process. By adhering to our guidelines, researchers can ensure that their cross-sectional studies possess sufficient statistical power to generate meaningful results. We acknowledge the significance of tailoring sample size calculations to the specific objectives and data characteristics of each study. Therefore, our approach is designed to be flexible and adaptable, accommodating the unique requirements of diverse research endeavors. There are several software options available for sample size calculation; however, we use the G*Power software for all the examples presented in this paper. Our guide is designed to provide practical understanding of the topic, with each step being accompanied by illustrative examples and detailed screenshots. This approach ensures that the material is not only understandable but also applicable in real-world scenarios. Furthermore, we take the extra step of interpreting every dialog box and screenshot, aiming to create a comfortable user experience with the software. We hope that this paper will serve as a valuable guide in the planning stage of a study, helping researchers to address a wider range of issues and reliably estimate the associations between selected exposures and the outcomes of interest with sufficient statistical power.