Every Coin Has Two Sides

An Introduction to Causal Inference in Pharmaceutical Statistics
1. Auflage 2026
ISBN: 978-1-041-06300-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 392 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Chapman & Hall/CRC Biostatistics Series

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 statics. 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

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Fang, Yixin

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Preface I An Introduction to Asymptotic Statistics 1 The Number Two 2 Fundamentals of Asymptotic Statistics 3 Statistical Inference 4 Maximum Likelihood Estimation (MLE) 5 Minimum Loss Estimation (MLE) II An Introduction to Causal Inference 6 Potential Outcomes 7 Study Designs 8 Estimand 9 Estimator 10 Sensitivity Analysis III An Introduction to Semiparametric Statistics 11 Regular and Asymptotically Linear Estimator 12 Efficient Influence Function 13 A Convenient Approach 14 Missing Data 15 Longitudinal Data IV An Introduction to Targeted Learning 16 Super Learning 17 Targeted Learning 18 Implementation 19 Intercurrent Events 20 Fusion of Two Cultures Bibliography Index

Über Autor(innen)

Yixin Fang is 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.