Statistical consideration in nephrology research

Nephrology research plays an important role in advancing our understanding of kidney disease and improving patient outcomes. However, the complexity of nephrology data and the application of advanced statistical methods present significant challenges. This review highlights key statistical considerations in nephrology research, focusing on common errors such as violations of statistical assumptions, multicollinearity, missing data, overfitting, and the integration of machine learning tools. It emphasizes the importance of applying appropriate statistical approaches to ensure the reliability of study findings. Additionally, the review underscores the need for transparency and reproducibility in nephrology research, particularly the importance of open access to data, code, and study protocols. By utilizing tools like R, RStudio, Git, and GitHub, researchers can integrate their code, results, and data into a transparent workflow, enhancing the reproducibility of their research. This review also presents a practical checklist for promoting reproducible research practices, which can help improve the quality, transparency, and reliability of nephrology studies. This review aims to contribute to the advancement of nephrology research and, ultimately, to support the long-term goal of improving patient care and outcomes.