
Every Coin Has Two Sides: An Introduction to Causal Inference in Pharmaceutical Statistics (Chapman & Hall/CRC Biostatistics Series)
Author(s): Yixin Fang (Author)
- Publisher Finelybook 出版社: Chapman and Hall/CRC
- Publication Date 出版日期: August 24, 2026
- Edition 版本: 1st
- Language 语言: English
- Print length 页数: 392 pages
- ISBN-10: 1041063008
- ISBN-13: 9781041063001
Book Description
Every Coin Has Two Sides: An Introduction to Causal Inference in Pharmaceutical Statistics introduces basic and advanced statistical inference methods and causal inference methods relevant to pharmaceutical statistics. This book distills seventy fundamental ideas and concepts―symbolized as gold coins, each with two sides―essential for mastering asymptotic statistics, causal inference, semiparametric statistics, and targeted learning. This book covers causal thinking that has increasingly become important in the planning, design, conduct, analysis, and interpretation of clinical studies. Progressing from basic statistical concepts to advanced targeted learning techniques, the book highlights the fusion of two cultures of statistical modelling. This book is suitable for graduate students in statistics, biostatistics, public health, and data science who are looking to pursue a career in the pharmaceutical industry, as well as for clinical statisticians and epidemiologists working in the pharmaceutical industry.
Key Features:
- Causal inference book for clinical statisticians in the pharmaceutical industry and graduate students
- Introduction to asymptotic statistics, causal inference, semiparametric statistics, and targeted learning
- Aligning with FDA and ICH guidance documents and covering different stages of clinical studies
- Seventy fundamental ideas and concepts―symbolized as gold coins, each with two sides
Editorial Reviews
Editorial Reviews
About the Author
Yixin Fangis Director of Statistics and Senior Research Fellow at AbbVie Inc. He obtained his Ph.D. in Statistics from Columbia University and is an experienced statistician and data scientist who has a history of working in both the biopharmaceutical industry and academia. He is an elected Fellow of the American Statistical Association.
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