Causal Inference in Pharmaceutical Statistics
Causal Inference in Pharmaceutical Statistics by Yixin Fang, published by CRC Press in 2024, is a comprehensive introduction to the essential concepts and methods of causal inference pertinent to pharmaceutical statistics. This edition, comprising 232 pages, explores various study designs commonly employed in the pharmaceutical industry, including randomized controlled trials and real-world evidence studies. The book addresses central questions in drug development and licensing, guiding readers through the fundamental principles and methodologies involved in conducting causal inference in clinical studies.
Readers will find a structured approach to understanding causal thinking as it applies to different stages of clinical studies, from planning and design to analysis and interpretation. The content is tailored for clinical statisticians and epidemiologists, making it a valuable resource for professionals in the pharmaceutical sector. Additionally, graduate students in statistics, biostatistics, and data science will benefit from the foundational insights provided, which align with FDA and ICH guidance documents. This book serves as a roadmap for those looking to navigate the complexities of causal inference in clinical research.
Official synopsis Publisher
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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