Methods in comparative effectiveness research
Author(s)
Bibliographic Information
Methods in comparative effectiveness research
（Chapman & Hall/CRC biostatistics series）（A Chapman & Hall book）
CRC, c2017
- : hardback
Available at 2 libraries
-
University of Toyama Library, Medical and Pharmaceutical Library図
: hardbackW74||G261m20182002001
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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.
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