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.

A practical guide to network meta-analysis with examples and code In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish which interventions are effective and cost-effective. Often a single study will not provide the answers and it is desirable to synthesise evidence from multiple sources, usually randomised controlled trials. This book takes an approach to evidence synthesis that is specifically intended for decision making when there are two or more treatment alternatives being evaluated, and assumes that the purpose of every synthesis is to answer the question "for this pre-identified population of patients, which treatment is 'best'?" A comprehensive, coherent framework for network meta-analysis (mixed treatment comparisons) is adopted and estimated using Bayesian Markov Chain Monte Carlo methods implemented in the freely available software WinBUGS. Each chapter contains worked examples, exercises, solutions and code that may be adapted by readers to apply to their own analyses. This book can be used as an introduction to evidence synthesis and network meta-analysis, its key properties and policy implications. Examples and advanced methods are also presented for the more experienced reader. Methods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised. Methods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal. Code presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons. Includes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output. Network Meta-Analysis for Decision Making will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.

Healthcare decision makers in search of reliable information that compares health interventions increasingly turn to systematic reviews for the best summary of the evidence. Systematic reviews identify, select, assess, and synthesize the findings of similar but separate studies, and can help clarify what is known and not known about the potential benefits and harms of drugs, devices, and other healthcare services. Systematic reviews can be helpful for clinicians who want to integrate research findings into their daily practices, for patients to make well-informed choices about their own care, for professional medical societies and other organizations that develop clinical practice guidelines. Too often systematic reviews are of uncertain or poor quality. There are no universally accepted standards for developing systematic reviews leading to variability in how conflicts of interest and biases are handled, how evidence is appraised, and the overall scientific rigor of the process. In Finding What Works in Health Care the Institute of Medicine (IOM) recommends 21 standards for developing high-quality systematic reviews of comparative effectiveness research. The standards address the entire systematic review process from the initial steps of formulating the topic and building the review team to producing a detailed final report that synthesizes what the evidence shows and where knowledge gaps remain. Finding What Works in Health Care also proposes a framework for improving the quality of the science underpinning systematic reviews. This book will serve as a vital resource for both sponsors and producers of systematic reviews of comparative effectiveness research.

Provides a comprehensive overview of range of approaches and methods available for synthesising qualitative and quantitative evidence and an explanation of why this is important. This book looks at different types of review and examining place of synthesis in reviews for policy and management decision making.

Practical guide for health practitioners and policy-makers, demystifying evidence-informed decision-making from the individual clinical level to global policy.

"Practitioners and patients are called upon to make numerous health care decisions and, in doing so, need to weigh various types of information before taking action. This information comes from a myriad of sources, including the results of well-designed research; information related to the preferences of patients/clients and their relevant others; the practitioner{u2019}s own experiences; and the nature and norms of the setting and culture in which the care is being delivered. Methods to synthesize qualitative evidence are now emerging and this text examines the methodological bases to qualitative synthesis and describes the processes involved in the conduct of a rigorous synthesis of qualitative evidence, with a particular focus on Meta-Aggregation."--[source inconnue].

The definitive work in D&I research -- now completely updated and expanded The application of scientific research to the creation of evidence-based policies is a science unto itself -- and one that is never easy. Dissemination and implementation research (D&I) is the study of how scientific advances can be implemented into everyday life, and understanding how it works has never been more important for students and professionals across the scientific, academic, and governmental communities. Dissemination and Implementation Research in Health is a practical guide to making research more consequential, a collection assembled and written by today's leading D&I researchers. Readers of this book are taught to: � Evaluate the evidence base in an effective intervention � Choose a strategy that produces the greatest impact � Design an appropriate and effectual study � Track essential outcomes � Account for the barriers to uptake in communities, social service agencies, and health care facilities The challenges to moving research into practice are universal, and they're complicated by the current landscape's reliance on partnerships and multi-center research. In this light, Dissemination and Implementation Research in Health is nothing less than a roadmap to effecting change in the sciences. It will have broad utility to researchers and practitioners in epidemiology, biostatistics, behavioral science, economics, medicine, social work, psychology, and anthropology -- both today and in our slightly better future.