Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009). CER is conducted to develop evidence that will aid patients, clinicians, purchasers, and health policy makers in making informed decisions at both the individual and population levels. CER encompasses a very broad range of types of studies—experimental, observational, prospective, retrospective, and research synthesis.
This volume covers the main areas of quantitative methodology for the design and analysis of CER studies. The volume has four major sections—causal inference; clinical trials; research synthesis; and specialized topics. The audience includes CER methodologists, quantitative-trained researchers interested in CER, and graduate students in statistics, epidemiology, and health services and outcomes research. The book assumes a masters-level course in regression analysis and familiarity with clinical research.
Observational studies. Data sources and design considerations for observational studies in CER. Causal inference methods in CER. Clinical trials in CER. CER considerations for clinical trials: Choice of research questions, populations, study settings, and endpoints. Approaches to randomization: cluster randomization, use of EMR. Adaptive designs, Bayesian methods. Deriving evidence for population subsets, CER and personalized medicine. Systematic reviews. General: systematic reviews with study-level and individual patient-level data. General: Strength and quality of evidence data. Network meta-analysis. Systematic reviews of diagnostic accuracy. Modeling. Decision analysis. Micro simulation methods. Cost-effectiveness analysis. Value of Information analysis. CER for diagnostic tests. Prevention studies. Early detection and surveillance studies.
". . . the book edited by Gatsonis and Morton fills a gap and seems very valuable for applied statisticians interested in the field of comparative effectiveness research particularly due to the selection of relevant topics and the scientific quality of the individual chapters written by leaders in the field."
~Christian Stock, Biometrical Journal
"In summary, the book covers a wide range of topics in CER with a good balance of methodology and practical considerations. Given that CER is a fast-growing area and is relatively unfamiliar to pharmaceutical statisticians, this book will be useful either as a general reference for those working in medical affairs or health technology assessment, or for those looking for a short introduction to a specific method (e.g., NMA)... In general, the book is quite readable without much prerequisite for statistical knowledge and will be useful to applied statisticians who are working in relevant areas or have an interest in CER."