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Evidence synthesis for decision making in healthcare

Statistics in Practice
Welton, Nicky J/Sutton, Alexander J/Cooper, Nicola J et al
Wiley-VCH GmbH
ISBN/EAN: 9780470061091
Umbreit-Nr.: 1163213

Sprache: Englisch
Umfang: 320 S.
Format in cm:
Einband: gebundenes Buch

Erschienen am 11.05.2012
Auflage: 1/2012
€ 67,90
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  • Zusatztext

    • In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are beneficial, are superior to all alternatives and are cost-effective. Usually one study will not provide answers to these questions and it will be necessary to synthesize evidence from multiple sources. This book aims to outline a coherent approach to such evidence synthesis, for the purpose of decision making. Each chapter contains worked examples, exercises and solutions drawn from a variety of medical disciplines Evidence Syntesis for Decision Making intends to provide a practical guide to the appropriate methods for synthesizing evidence for use in analytical decision models. More specifically, it proposes a comprehensive evidence synthesis framework, which models all the available data appropriately and efficiently in a format that can be incorporated directly into a decision model.

  • Kurztext

    • In the evaluation of healthcare, rigorous methods of quantitative assessment arenecessary 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.

  • Autorenportrait
    • InhaltsangabePreface 1. INTRODUCTION 1.1. The rise of health economics 1.2. Decision-making under uncertainty 1.3. Evidence-based medicine 1.4. Bayesian statistics 1.5. NICE 1.6. About this book 1.7. Summary key points 1.8. Further reading 2. BAYESIAN METHODS AND WINBUGS 2.1. Introduction to Bayesian methods 2.2. Introduction to WinBUGS 2.3. Advantages and disadvantages of a Bayesian approach 2.4. Summary key points 2.5. Further reading 2.6. Exercises 3. INTRODUCTION TO DECISION MODELS 3.1. Introduction 3.2. Decision tree models 3.3. Model parameters 3.4. Deterministic decision tree 3.5. Stochastic decision tree 3.6. Sources of evidence 3.7. Principles of synthesis for decision models (motivation for the rest of the book) 3.8. Summary key points 3.9. Further reading 3.10. Exercises 4. METAANALYSIS USING BAYESIAN METHODS 4.1. Introduction 4.2. Fixed effect model 4.3. Random effects model 4.4. Publication bias 4.5. Study validity 4.6. Summary key points 4.7. Further reading 4.8. Exercises 5. EXPLORING BETWEEN STUDY HETEROGENEITY 5.1. Introduction 5.2. Random effects meta-regression models 5.3. Limitations of meta-regression 5.4. Baseline risk 5.5. Summary key points 5.6. Further reading 5.7. Exercises 6. MODEL CRITIQUE AND EVIDENCE CONSISTENCY IN RANDOM EFFECTS META-ANALYSIS 6.1. Introduction 6.2. The random effects model revisited 6.3. Assessing model fit 6.4. Model comparison 6.5. Exploring inconsistency 6.6. Summary key points 6.7. Further reading 6.8. Exercises 7. EVIDENCE SYNTHESIS IN A DECISION MODELLING FRAMEWORK 7.1. Introduction 7.2. Evaluation of decision models: one-stage vs two-stage 7.3. Sensitivity analyses (of model inputs and model specifications) 7.4. Summary key points 7.5. Further reading 7.6. Exercises 8. MULTIPARAMETER EVIDENCE SYNTHESIS IN EPIDEMIOLOGICAL MODELS 8.1. Introduction 8.2. Prior and posterior simulation in a probabilistic model: maple syrup urine disease - MSUD 8.3. A model for prenatal HIV testing 8.4. Model criticism in multi-parameter models 8.5. Evidence-based policy 8.6. Summary key points 8.7. Further reading 8.8. Exercises 9. MIXED TREATMENT COMPARISONS 9.1. Why go beyond "direct" head-to-head trials? 9.2. A fixed treatment effect model for MTC 9.3. Random effect MTC models 9.4. Model choice and consistency of MTC evidence 9.5. Multiarm trials 9.6. Assumptions made in MTC 9.7. Embedding an MTC within a cost-effectiveness analysis 9.8. Extension to continuous, rate and other outcomes 9.9. Key points 9.10. Further reading 9.11. Exercises 10. MARKOV MODELS 10.1. Introduction 10.2. Continuous and discrete time Markov models 10.3. Decision analysis with Markov models 10.4. Estimating transition parameters from a single study 10.5. Propagating uncertainty in Markov parameters into a decision model 10.6. Estimating transition parameters from a synthesis of several studies 10.7. Summary key points 10.8. Further reading 10.9. Exercises 11. GENERALISED EVIDENCE SYNTHESIS 11.1. Introduction 11.2. Deriving a prior distribution from observational evidence 11.3. Bias allowance model for the observational data 11.4. Hierarchical models for evidence from different study designs 11.5. Discussion 11.6. Summary key points 11.7. Further reading 11.8. Exercises 12. EXPECTED VALUE OF INFORMATION FOR RESEARCH PRIORITISATION AND STUDY DESIGN 12.1. Introduction 12.2. Expected value of perfect information 12.3. Expected value of partial perfect information 12.4. Expected value of sample information 12.5. Expected net benefit of sampling 12.6. Summary key points 12.7. Further reading 12.8. Exercises APPENDICES Appendix A1: Abbreviations Appendix A2: Common Distributions NOMENCLATURE/NOTATION