Korvink M. Practical Healthcare Statistics with Examples in Python and R...2025

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Korvink M. Practical Healthcare Statistics with Examples in Python and R...2025

Textbook in PDF format Practical Healthcare Statistics with Examples in Python and R provides a clear and straightforward introduction to statistical methods in healthcare. Designed for recent graduates, new analysts, and professionals transitioning into healthcare analytics, it offers practical guidance on tackling real-world problems using statistical concepts and programming. The book is divided into three primary sections. The first section provides an introduction to healthcare data and measures. In these chapters, readers will learn about the nuances of administrative claims and electronic health records, as well as common industry measures related to quality and efficiency of care. The second section will cover foundational techniques, such as hypothesis testing and regression analysis, as well as more advanced approaches, like generalized additive models and hierarchical models. In the last section, readers will be introduced to epidemiological techniques such as direct and indirect standardization, measures of disease frequency and association, and time-to-event analysis. The book emphasizes interpretable methods that are both effective and easy to communicate to clinical and non-technical stakeholders. Each technique presented in the book is accompanied by statistical notation described in plain English, as well as a self-contained example implemented in both Python and R. These examples help readers connect statistical methods to real healthcare scenarios without requiring extensive programming experience. By working through these examples, readers will build technical skills and a practical understanding of how to analyze healthcare data. I will also assume that the reader is a beginner in Python or R, at a minimum. We will not discuss setting up a development environment or using the many IDEs available. Furthermore, no crash course on Python or R programming is provided in these pages. There are many well-written resources for those beginning Python and R-based data analysis, and duplicating those efforts here would take away from the healthcare-focused analysis this book is designed to provide. Finally, this book will not discuss data “munging” or data processing to prepare analytic datasets. There are a multitude of books on data analysis using Python or R, and it is advised that readers seek those resources for foundational knowledge on data analysis. We will use code only as a means to an end and will use the core data manipulation and statistical packages when possible. In Python, examples primarily use Pandas, NumPy, statsmodels, scipy.stats, and Scikit-learn. In R, we will use core base R functions when possible and will rely on mature, well-supported libraries when no base R support is available (e.g., mcgv, lme4, survival). The examples provided will be procedural (for ease of reproducibility) using arrays and data frames, and as such, we will avoid examples using object-oriented approaches. These methods are not only central to improving patient care but are also adaptable to other areas within and beyond healthcare. This book is a practical resource for analysts, data scientists, health researchers, and others looking to make informed, data-driven decisions in healthcare