Evidence Synthesis for Decision Making in Healthcare

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. <p><i>Key features:</i></p> <ul> <li>A coherent approach to evidence synthesis from multiple sources.</li> <li>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.</li> <li>Provides methods to statistically combine evidence from a range of evidence structures.</li> <li>Emphasizes the importance of model critique and checking for evidence consistency.</li> <li>Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.</li> <li>WinBUGS code is provided for all examples.</li> </ul> <p><i>Evidence Synthesis for Decision Making in Healthcare</i> is intended for health economists, decision modelers, statisticians and others involved in evidence synthesis, health technology assessment, and economic evaluation of health technologies.</p>