Comparative Effectiveness and Personalized Medicine Research Using Real-World Data
1st Edition
Published on 4. May 2026
621 pages
978-1-040-74701-8 (ISBN)
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. 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, 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.
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, 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.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
College/higher education
Product notice
Reflowable
Illustrations
32 Tables, black and white; 48 Line drawings, color; 52 Line drawings, black and white; 48 Illustrations, color; 52 Illustrations, black and white
File size
22,51 MB
ISBN-13
978-1-040-74701-8 (9781040747018)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Thomas P. A. Debray | Tri-Long Nguyen | Robert W. Platt
Comparative Effectiveness and Personalized Medicine Research Using Real-World Data
Book
05/2026
1st Edition
Chapman & Hall/CRC
€217.50
Shipment within 10-20 days
Persons
Thomas Debray, PhD, is a distinguished statistician and academic who transitioned from academia into specialized consulting for the pharmaceutical industry, leveraging his expertise in real-world evidence, precision medicine, and evidence synthesis to drive innovation in clinical research. He is currently affiliated with the University Medical Center G?ottingen as a Guest Scientist, where he continues to collaborate on methodological research together with Prof. Tim Friede. He previously served as an Assistant Professor at Utrecht University, and held honorary positions at University College London and the University of Oxford. Furthermore, he served as an Associate Editor for Diagnostic and Prognostic Research, where he played a pivotal role in promoting the dissemination of high-quality prediction modeling research.
During his academic tenure, Dr. Debray collaborated with leading experts such as Prof. Carl Moons, Prof. Richard Riley, and Prof. Ewout Steyerberg to advance meta-analysis methods for risk prediction. He contributed to Cochrane methodological guidelines, led the EU-funded RECODID project on data harmonization, and co-developed the TRIPODCluster reporting guidelines to improve transparency in clustered prediction model studies.
He has also been active within the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP), helping to strengthen methodological standards in pharmacoepidemiology and real-world evidence research. As the founder of Smart Data Analysis and Statistics, Thomas leads a team dedicated to advancing clinical trial methodologies and providing tailored statistical support to pharmaceutical companies and biotech firms. The company focuses on non-standard, innovative approaches to clinical trial design, evidence synthesis, comparative effectiveness research, and simulation-based decision making-methods that go beyond traditional analyses to address complex regulatory and scientific challenges. Through its active role in international research consortia and grants, such as the Marie Sk lodowska-Curie SHARE-CTD initiative on data sharing in clinical trials, SDAS provides a gateway for academia, industry, HTA bodies, and regulators to interact and co-develop innovative solutions.
As the founder of Smart Data Analysis and Statistics, Thomas leads a team dedicated to advancing clinical trial methodologies and providing tailored statistical support to pharmaceutical companies and biotech firms. The company focuses on non-standard, innovative approaches to clinical trial design, evidence synthesis, comparative effectiveness research, and simulation-based decision making-methods that go beyond traditional analyses to address complex regulatory and scientific challenges. Through its active role in international research consortia and grants, such as the Marie Sk lodowska-Curie SHARE-CTD initiative on data sharing in clinical trials, SDAS provides a gateway for academia, ndustry, HTA bodies, and regulators to interact and co-develop innovative solutions.
Long Nguyen, PhD, is an Associate Professor in the Section of Epidemiology at the University of Copenhagen. His research bridges epidemiological and statistical methodologies, focusing on causal inference and prediction modeling with applications in medicine and public health. Dr. Nguyen is committed to science democratization and public engagement through culture and the arts, exemplified by his creation of nor ? bro, a novel platform in Copenhagen's Norrebro neighborhood that fosters societal reflections through workshops, art exhibitions, and community events.
In his teaching role, Dr. Nguyen leads and contributes to courses at the University of Copenhagen, focusing on themes such as innovative teaching methods, causal inference,
precision medicine, and advanced epidemiological techniques. His dedication to education was recognized with a nomination for the Teacher of the Year Award (?Arets Harald) in 2023.
Dr. Nguyen's interdisciplinary approach and commitment to integrating diverse knowledge domains significantly contribute to advancing clinical epidemiology and public health engagement.
Robert Platt, PhD, is Director of the School of Population and Global Health, and holds the Albert Boehringer Chair in Pharmacoepidemiology at McGill University. His research focuses on developing advanced statistical methods for causal inference in observational studies and clinical trial data, with particular emphasis on pharmacoepidemiology, pediatric, and perinatal epidemiology. Prof. Platt is the Principal Investigator of the Canadian Network for Observational Drug Effect Studies (CNODES) and led the CNODES Methods Team from 2011 to 2022. Over its lifespan, CNODES has addressed more than 80 queries from Canadian and international stakeholders on drug safety and effectiveness.
Prof. Platt has authored or co-authored over 400 peer-reviewed articles on statistical methodology and applications. He has supervised more than 50 research trainees in epidemiology,
biostatistics, and statistics, fostering the next generation of researchers in these fields. Dr. Platt has served as President of both the Society for Pediatric and Perinatal Epidemiologic
Research and the Statistical Society of Canada. He is a Fellow of the American Statistical Association and the International Society for Pharmacoepidemiology, recognizing his significant contributions to statistical science and public health research.
In addition to his research and mentorship, Prof. Platt plays a pivotal role in academic publishing. He is an Editor-in-Chief of Statistics in Medicine, an editor for the American Journal of Epidemiology, and an Associate Editor for both the International Journal of Biostatistics and Pharmacoepidemiology and Drug Safety. His leadership and expertise continue to shape the fields of epidemiology and biostatistics globally.
During his academic tenure, Dr. Debray collaborated with leading experts such as Prof. Carl Moons, Prof. Richard Riley, and Prof. Ewout Steyerberg to advance meta-analysis methods for risk prediction. He contributed to Cochrane methodological guidelines, led the EU-funded RECODID project on data harmonization, and co-developed the TRIPODCluster reporting guidelines to improve transparency in clustered prediction model studies.
He has also been active within the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP), helping to strengthen methodological standards in pharmacoepidemiology and real-world evidence research. As the founder of Smart Data Analysis and Statistics, Thomas leads a team dedicated to advancing clinical trial methodologies and providing tailored statistical support to pharmaceutical companies and biotech firms. The company focuses on non-standard, innovative approaches to clinical trial design, evidence synthesis, comparative effectiveness research, and simulation-based decision making-methods that go beyond traditional analyses to address complex regulatory and scientific challenges. Through its active role in international research consortia and grants, such as the Marie Sk lodowska-Curie SHARE-CTD initiative on data sharing in clinical trials, SDAS provides a gateway for academia, industry, HTA bodies, and regulators to interact and co-develop innovative solutions.
As the founder of Smart Data Analysis and Statistics, Thomas leads a team dedicated to advancing clinical trial methodologies and providing tailored statistical support to pharmaceutical companies and biotech firms. The company focuses on non-standard, innovative approaches to clinical trial design, evidence synthesis, comparative effectiveness research, and simulation-based decision making-methods that go beyond traditional analyses to address complex regulatory and scientific challenges. Through its active role in international research consortia and grants, such as the Marie Sk lodowska-Curie SHARE-CTD initiative on data sharing in clinical trials, SDAS provides a gateway for academia, ndustry, HTA bodies, and regulators to interact and co-develop innovative solutions.
Long Nguyen, PhD, is an Associate Professor in the Section of Epidemiology at the University of Copenhagen. His research bridges epidemiological and statistical methodologies, focusing on causal inference and prediction modeling with applications in medicine and public health. Dr. Nguyen is committed to science democratization and public engagement through culture and the arts, exemplified by his creation of nor ? bro, a novel platform in Copenhagen's Norrebro neighborhood that fosters societal reflections through workshops, art exhibitions, and community events.
In his teaching role, Dr. Nguyen leads and contributes to courses at the University of Copenhagen, focusing on themes such as innovative teaching methods, causal inference,
precision medicine, and advanced epidemiological techniques. His dedication to education was recognized with a nomination for the Teacher of the Year Award (?Arets Harald) in 2023.
Dr. Nguyen's interdisciplinary approach and commitment to integrating diverse knowledge domains significantly contribute to advancing clinical epidemiology and public health engagement.
Robert Platt, PhD, is Director of the School of Population and Global Health, and holds the Albert Boehringer Chair in Pharmacoepidemiology at McGill University. His research focuses on developing advanced statistical methods for causal inference in observational studies and clinical trial data, with particular emphasis on pharmacoepidemiology, pediatric, and perinatal epidemiology. Prof. Platt is the Principal Investigator of the Canadian Network for Observational Drug Effect Studies (CNODES) and led the CNODES Methods Team from 2011 to 2022. Over its lifespan, CNODES has addressed more than 80 queries from Canadian and international stakeholders on drug safety and effectiveness.
Prof. Platt has authored or co-authored over 400 peer-reviewed articles on statistical methodology and applications. He has supervised more than 50 research trainees in epidemiology,
biostatistics, and statistics, fostering the next generation of researchers in these fields. Dr. Platt has served as President of both the Society for Pediatric and Perinatal Epidemiologic
Research and the Statistical Society of Canada. He is a Fellow of the American Statistical Association and the International Society for Pharmacoepidemiology, recognizing his significant contributions to statistical science and public health research.
In addition to his research and mentorship, Prof. Platt plays a pivotal role in academic publishing. He is an Editor-in-Chief of Statistics in Medicine, an editor for the American Journal of Epidemiology, and an Associate Editor for both the International Journal of Biostatistics and Pharmacoepidemiology and Drug Safety. His leadership and expertise continue to shape the fields of epidemiology and biostatistics globally.
Content
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.
