Applied Statistical Methods for Health Sciences Research

A graduate textbook

Welcome

This is the online version of Applied Statistical Methods for Health Sciences Research by The rgtlab Curriculum Project, a graduate textbook.

The book covers the applied methodological core of an MS-level biostatistics curriculum: estimands and study design, causal inference for observational data, mediation, longitudinal and survival analysis at applied depth, clinical trial design and analysis, missing-data methodology, meta-analysis, and advanced categorical-data methods. It is positioned between the workflow-focused Practicum and the computing-focused SCAI volumes on the methods axis.

The book sits in a five-volume graduate sequence:

• R for Biostatistics: A One-Week Boot Camp — pre-program preparation.
• Biostatistics Practicum — workflow infrastructure.
• Statistical Computing in the Age of AI — introductory methods and computing.
• Advanced Statistical Computing in the Age of AI — advanced numerical and Bayesian computation.
• Applied Generative AI for Health Sciences Research — generative AI as the orthogonal axis.
• Applied Statistical Methods for Health Sciences Research (this volume) — the applied-methods axis.

See the Preface for motivation and the Conventions page for visual cues.

License

This book is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International.

Code samples are licensed under Creative Commons CC0 1.0 Universal, i.e. public domain.