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.
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.
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.
Inhaltsangabe
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
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
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