Comparative Effectiveness and Personalized Medicine Research Using Real-World Data (Chapman & Hall/crc Biostatistics Series)

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. Comparative Effectiveness and Personalized Medicine Research Using Real-World Data 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 text serves as an essential resource for healthcare researchers, biostatisticia