Full Description
Comparative Effectiveness Research has become essential for shaping regulatory policy, informing economic evaluations, and guiding treatment standardization across healthcare systems worldwide. While traditional randomized controlled trials remain the cornerstone of evidence generation, they have important limitations when treatment effects are heterogeneous. In practice, patients often respond differently to the same intervention due to differences in demographics, genetics, comorbidities, and other contextual factors. This variability creates challenges for personalized medicine and limits the generalizability of trial results to diverse real-world patient populations.
This comprehensive guide addresses these critical gaps by exploring how real-world data from electronic health records, patient registries, and observational studies can transform comparative effectiveness research and enable truly personalized treatment decisions. The book provides detailed methodological frameworks for moving beyond traditional subgroup analyses to sophisticated approaches that harness the full potential of real-world evidence for individualized patient care.
Features:
• Comprehensive coverage of statistical techniques for analyzing heterogeneity of treatment effects and estimating individualized treatment effects
• Innovative approaches for combining diverse data sources to generate robust comparative effectiveness evidence
• Cutting-edge algorithms and computational methods for personalized medicine applications
• Real-world examples, case studies, and vignettes demonstrating successful applications in clinical practice
• Access to dedicated online training materials, example code, and supplementary content through companion websites
• Current guidance from FDA, EMA, and other regulatory bodies on real-world evidence in decision-making
This book serves as an essential resource for healthcare researchers, biostatisticians, epidemiologists, health economists, regulatory professionals, and clinicians seeking to understand and implement advanced methodologies in comparative effectiveness research. Written through collaboration between leading experts in healthcare research, biostatistics, epidemiology, and health policy, it provides both theoretical foundations and practical tools for leveraging real-world data in evidence generation. The book is particularly valuable for professionals involved in regulatory submissions, health technology assessments, treatment guideline development, and personalized medicine initiatives, offering the methodological rigor needed to enhance the credibility and reliability of real-world evidence in healthcare decision-making.
Contents
1 Introduction to RWD and RWE for decision making in health care. 2 Case studies. 3 Validity control and quality assessment of real-world data and real-world evidence. 4 Introduction to Directed Acyclic Graphs (DAGs) for bias visualization. 5 Understanding and defining causal effects. 6 Estimands. 7 Confounding adjustment using propensity score methods. 8 Effect Modification in Non-Randomized Studies. 9 Confounding adjustment using prognostic score methods. 10 Dealing with missing data. 11 Principles of meta-analysis and indirect treatment comparisons. 12 Systematic review and meta-analysis of Real-World Evidence. 13 Individual Participant Data Meta-analysis of clinical trials and real-world data. 14 Dealing with irregular and informative visits. 15 Dealing with measurement error. 16 The role of machine learning in real-world evidence generation. 17 Introduction to methods for personalizing medicine. 18 Modeling Personalized Treatment Effects Using Multiple Data Sources. 19 Validation of prediction models for patient outcomes and individualized treatment effect. 20 Visualization and interpretation of individualized treatment rule results. 21 Digital Health in Real-World Data: Challenges and Opportunities. 22 RWE in regulatory and reimbursement decision-making. 23 Concluding remarks: Putting methods to practice.
