Summary and Info
In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are both effective and cost-effective. Usually a single study will not fully address these issues and it is desirable to synthesize evidence from multiple sources. This book aims to provide a practical guide to evidence synthesis for the purpose of decision making, starting with a simple single parameter model, where all studies estimate the same quantity (pairwise meta-analysis) and progressing to more complex multi-parameter structures (including meta-regression, mixed treatment comparisons, Markov models of disease progression, and epidemiology models). A comprehensive, coherent framework is adopted and estimated using Bayesian methods.Key features:A coherent approach to evidence synthesis from multiple sources.Focus is given to Bayesian methods for evidence synthesis that can be integrated within cost-effectiveness analyses in a probabilistic framework using Markov Chain Monte Carlo simulation.Provides methods to statistically combine evidence from a range of evidence structures.Emphasizes the importance of model critique and checking for evidence consistency.Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.WinBUGS code is provided for all examples. Evidence Synthesis for Decision Making in Healthcare is intended for health economists, decision modelers, statisticians and others involved in evidence synthesis, health technology assessment, and economic evaluation of health technologies.Content: Chapter 1 Introduction (pages 1–16): Chapter 2 Bayesian Methods and WinBUGS (pages 17–42): Chapter 3 Introduction to Decision Models (pages 43–75): Chapter 4 Meta?Analysis Using Bayesian Methods (pages 76–93): Chapter 5 Exploring Between Study Heterogeneity (pages 94–114): Chapter 6 Model Critique and Evidence Consistency in Random Effects Meta?Analysis (pages 115–137): Chapter 7 Evidence Synthesis in a Decision Modelling Framework (pages 138–150): Chapter 8 Multi?Parameter Evidence Synthesis (pages 151–168): Chapter 9 Mixed and Indirect Treatment Comparisons (pages 169–192): Chapter 10 Markov Models (pages 193–226): Chapter 11 Generalised Evidence Synthesis (pages 227–250): Chapter 12 Expected Value of Information for Research Prioritization and Study Design (pages 251–269):
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