Causal Inference in Pharmaceutical Statistics introduces the basic concepts and fundamental methods of causal inference relevant to pharmaceutical statistics. This book covers causal thinking for different types of commonly used study designs in the pharmaceutical industry, including but not limited to randomized controlled clinical trials, longitudinal studies, singlearm clinical trials with external controls, and real-world evidence studies. The book starts with the central questions in drug development and licensing, takes the reader through the basic concepts and methods via different study types and through different stages, and concludes with a roadmap to conduct causal inference in clinical studies. The book is intended for clinical statisticians and epidemiologists working in the pharmaceutical industry. It will also be useful to graduate students in statistics, biostatistics, and data science looking to pursue a career in the pharmaceutical industry.
Key Features:
- Causal inference book for clinical statisticians in the pharmaceutical industry
- Introductory level on the most important concepts and methods
- Align with FDA and ICH guidance documents
- Across different stages of clinical studies: plan, design, conduct, analysis, and interpretation
- Cover a variety of commonly used study designs
Preface
1. Introduction
2. Randomized Controlled Clinical Trials
3. Missing Data Handling
4. Intercurrent Events Handling
5. Longitudinal Studies
6. Real-World Evidence Studies
7. The Art of Estimation (I): M-estimation
8. The Art of Estimation (II): TMLE
9. The Art of Estimation (III): LTMLE
10. Sensitivity Analysis
11. A Roadmap for Causal Inference
12. Applications of the Roadmap
Bibliography
Index
Biography
Yixin Fang, Ph.D. is Director of Statistics and 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.
