- 246 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Causal Inference in Pharmaceutical Statistics
About this book
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
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Information
Table of contents
- Cover Page
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- 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