2025/11/11 by Amit K. Chowdhry · 1 voice
Mathematics · Pharmacology, Toxicology and Pharmaceutics · #Advanced Causal Inference Techniques #Pharmacovigilance and Adverse Drug Reactions #Statistical Methods in Clinical Trials
paper · pdf · doi:10.1093/jrsssa/qnaf184
openalex created_date 2025/11/11 · openalex publication_date 2025/11/11 · openalex updated_date 2026/07/30
The stated objective of this book is to educate the pharmaceutical statistician about causal inference. To this end, the author is very successful. Many in clinical trials dismiss causal inference as only being useful for observational studies, but not for clinical trials. In this book, the author describes the rationale for causal inference, along with the mathematical foundation for understanding and using it in practice. Historically, the clinical trials community has relied on randomization for causal inference, but there are many instances for which randomization is insufficient for causal inference, e.g., missing data and nonadherence. For these situations, causal inference methods may be useful. For advanced undergraduate students and graduate students, I recommend they learn causal inference from the same book used by the author of this book to learn causal inference: Causal Inference by Hernan and Robins. Then, if proceeding to a career in the pharmaceutical industry, this book is an excellent supplement which covers considerations which are useful to the pharmaceutical statistician. Particular strengths of this book include coverage of advanced topics not covered in many other books on the topic. These include intercurrent events, M-estimation, TMLE, and sensitivity analysis. In summary, this book is a useful reference for statisticians in the pharmaceutical industry. I would recommend it as an advanced text, after one has already read an introductory text. There are no data for this book review.