Details

Evidence Synthesis for Decision Making in Healthcare


Evidence Synthesis for Decision Making in Healthcare


Statistics in Practice, Band 127 1. Aufl.

von: Nicky J. Welton, Alexander J. Sutton, Nicola Cooper, Keith R. Abrams, A. E. Ades

61,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 12.04.2012
ISBN/EAN: 9781118305409
Sprache: englisch
Anzahl Seiten: 320

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Beschreibungen

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>
<p>Preface xi</p> <p><b>1 Introduction 1</b></p> <p>1.1 The rise of health economics 1</p> <p>1.2 Decision making under uncertainty 4</p> <p>1.2.1 Deterministic models 4</p> <p>1.2.2 Probabilistic decision modelling 6</p> <p>1.3 Evidence-based medicine 9</p> <p>1.4 Bayesian statistics 10</p> <p>1.5 NICE 11</p> <p>1.6 Structure of the book 12</p> <p>1.7 Summary key points 13</p> <p>1.8 Further reading 13</p> <p>References 14</p> <p><b>2 Bayesian methods and WinBUGS 17</b></p> <p>2.1 Introduction to Bayesian methods 17</p> <p>2.1.1 What is a Bayesian approach? 17</p> <p>2.1.2 Likelihood 18</p> <p>2.1.3 Bayes’ theorem and Bayesian updating 19</p> <p>2.1.4 Prior distributions 22</p> <p>2.1.5 Summarising the posterior distribution 23</p> <p>2.1.6 Prediction 24</p> <p>2.1.7 More realistic and complex models 24</p> <p>2.1.8 MCMC and Gibbs sampling 25</p> <p>2.2 Introduction to WinBUGS 26</p> <p>2.2.1 The BUGS language 26</p> <p>2.2.2 Graphical representation 31</p> <p>2.2.3 Running WinBUGS 32</p> <p>2.2.4 Assessing convergence in WinBUGS 33</p> <p>2.2.5 Statistical inference in WinBUGS 36</p> <p>2.2.6 Practical aspects of using WinBUGS 39</p> <p>2.3 Advantages and disadvantages of a Bayesian approach 39</p> <p>2.4 Summary key points 40</p> <p>2.5 Further reading 41</p> <p>2.6 Exercises 41</p> <p>References 42</p> <p><b>3 Introduction to decision models 43</b></p> <p>3.1 Introduction 43</p> <p>3.2 Decision tree models 44</p> <p>3.3 Model parameters 45</p> <p>3.3.1 Effects of interventions 45</p> <p>3.3.2 Quantities relating to the clinical epidemiology of the clinical condition being treated 50</p> <p>3.3.3 Utilities 52</p> <p>3.3.4 Resource use and costs 52</p> <p>3.4 Deterministic decision tree 52</p> <p>3.5 Stochastic decision tree 56</p> <p>3.5.1 Presenting the results of stochastic economic decision models 60</p> <p>3.6 Sources of evidence 66</p> <p>3.7 Principles of synthesis for decision models (motivation for the rest of the book) 70</p> <p>3.8 Summary key points 70</p> <p>3.9 Further reading 71</p> <p>3.10 Exercises 71</p> <p>References 72</p> <p><b>4 Meta-analysis using Bayesian methods 76</b></p> <p>4.1 Introduction 76</p> <p>4.2 Fixed Effect model 78</p> <p>4.3 Random Effects model 81</p> <p>4.3.1 The predictive distribution 83</p> <p>4.3.2 Prior specification for τ 84</p> <p>4.3.3 ‘Exact’ Random Effects model for Odds Ratios based on a Binomial likelihood 84</p> <p>4.3.4 Shrunken study level estimates 86</p> <p>4.4 Publication bias 87</p> <p>4.5 Study validity 88</p> <p>4.6 Summary key points 88</p> <p>4.7 Further reading 88</p> <p>4.8 Exercises 89</p> <p>References 92</p> <p><b>5 Exploring between study heterogeneity 94</b></p> <p>5.1 Introduction 94</p> <p>5.2 Random effects meta-regression models 95</p> <p>5.2.1 Generic random effect meta-regression model 95</p> <p>5.2.2 Random effects meta-regression model for Odds Ratio (OR) outcomes using a Binomial likelihood 98</p> <p>5.2.3 Autocorrelation and centring covariates 100</p> <p>5.3 Limitations of meta-regression 104</p> <p>5.4 Baseline risk 105</p> <p>5.4.1 Model for including baseline risk in a meta-regression on the (log) OR scale 107</p> <p>5.4.2 Final comments on including baseline risk as a covariate 109</p> <p>5.5 Summary key points 110</p> <p>5.6 Further reading 110</p> <p>5.7 Exercises 110</p> <p>References 113</p> <p><b>6 Model critique and evidence consistency in random effects meta-analysis 115</b></p> <p>6.1 Introduction 115</p> <p>6.2 The Random Effects model revisited 117</p> <p>6.3 Assessing model fit 121</p> <p>6.3.1 Deviance 121</p> <p>6.3.2 Residual deviance 122</p> <p>6.4 Model comparison 124</p> <p>6.4.1 Effective number of parameters, pD 125</p> <p>6.4.2 Deviance Information Criteria 126</p> <p>6.5 Exploring inconsistency 127</p> <p>6.5.1 Cross-validation 128</p> <p>6.5.2 Mixed predictive checks 131</p> <p>6.6 Summary key points 134</p> <p>6.7 Further reading 134</p> <p>6.8 Exercises 134</p> <p>References 137</p> <p><b>7 Evidence synthesis in a decision modelling framework 138</b></p> <p>7.1 Introduction 138</p> <p>7.2 Evaluation of decision models: One-stage vs two-stage approach 139</p> <p>7.3 Sensitivity analyses (of model inputs and model specifications) 147</p> <p>7.4 Summary key points 147</p> <p>7.5 Further reading 147</p> <p>7.6 Exercises 147</p> <p>References 149</p> <p><b>8 Multi-parameter evidence synthesis 151</b></p> <p>8.1 Introduction 151</p> <p>8.2 Prior and posterior simulation in a probabilistic model: Maple Syrup Urine Disease (MSUD) 152</p> <p>8.3 A model for prenatal HIV testing 155</p> <p>8.4 Model criticism in multi-parameter models 161</p> <p>8.5 Evidence-based policy 163</p> <p>8.6 Summary key points 164</p> <p>8.7 Further reading 165</p> <p>8.8 Exercises 166</p> <p>References 167</p> <p><b>9 Mixed and indirect treatment comparisons 169</b></p> <p>9.1 Why go beyond ‘direct’ head-to-head trials? 169</p> <p>9.2 A fixed treatment effects model for MTC 172</p> <p>9.2.1 Absolute treatment effects 176</p> <p>9.2.2 Relative treatment efficacy and ranking 176</p> <p>9.3 Random Effects MTC models 178</p> <p>9.4 Model choice and consistency of MTC evidence 179</p> <p>9.4.1 Techniques for presenting and understanding the results of MTC 180</p> <p>9.5 Multi-arm trials 181</p> <p>9.6 Assumptions made in mixed treatment comparisons 182</p> <p>9.7 Embedding an MTC within a cost-effectiveness analysis 183</p> <p>9.8 Extension to continuous, rate and other outcomes 185</p> <p>9.9 Summary key points 187</p> <p>9.10 Further reading 187</p> <p>9.11 Exercises 189</p> <p>References 190</p> <p><b>10 Markov models 193</b></p> <p>10.1 Introduction 193</p> <p>10.2 Continuous and discrete time Markov models 195</p> <p>10.3 Decision analysis with Markov models 196</p> <p>10.3.1 Evaluating Markov models 197</p> <p>10.4 Estimating transition parameters from a single study 199</p> <p>10.4.1 Likelihood 202</p> <p>10.4.2 Priors and posteriors for multinomial probabilities 202</p> <p>10.5 Propagating uncertainty in Markov parameters into a decision model 206</p> <p>10.6 Estimating transition parameters from a synthesis of several studies 209</p> <p>10.6.1 Challenges for meta-analysis of evidence on Markov transition parameters 209</p> <p>10.6.2 The relationship between probabilities and rates 211</p> <p>10.6.3 Modelling study effects 213</p> <p>10.6.4 Synthesis of studies reporting aggregate data 215</p> <p>10.6.5 Incorporating studies that provide event history data 217</p> <p>10.6.6 Reporting results from a Random Effects model 219</p> <p>10.6.7 Incorporating treatment effects 220</p> <p>10.7 Summary key points 224</p> <p>10.8 Further reading 224</p> <p>10.9 Exercises 224</p> <p>References 225</p> <p><b>11 Generalised evidence synthesis 227</b></p> <p>11.1 Introduction 227</p> <p>11.2 Deriving a prior distribution from observational evidence 230</p> <p>11.3 Bias allowance model for the observational data 233</p> <p>11.4 Hierarchical models for evidence from different study designs 238</p> <p>11.5 Discussion 244</p> <p>11.6 Summary key points 244</p> <p>11.7 Further reading 245</p> <p>11.8 Exercises 246</p> <p>References 248</p> <p><b>12 Expected value of information for research prioritisation and study design 251</b></p> <p>12.1 Introduction 251</p> <p>12.2 Expected value of perfect information 256</p> <p>12.3 Expected value of partial perfect information 259</p> <p>12.3.1 Computation 261</p> <p>12.3.2 Notes on EVPPI 264</p> <p>12.4 Expected value of sample information 264</p> <p>12.4.1 Computation 265</p> <p>12.5 Expected net benefit of sampling 266</p> <p>12.6 Summary key points 267</p> <p>12.7 Further reading 268</p> <p>12.8 Exercises 268</p> <p>References 268</p> <p>Appendix 1 Abbreviations 270</p> <p>Appendix 2 Common distributions 272</p> <p>A2.1 The Normal distribution 272</p> <p>A2.2 The Binomial distribution 273</p> <p>A2.3 The Multinomial distribution 273</p> <p>A2.4 The Uniform distribution 274</p> <p>A2.5 The Exponential distribution 274</p> <p>A2.6 The Gamma distribution 275</p> <p>A2.7 The Beta distribution 276</p> <p>A2.8 The Dirichlet distribution 277</p> <p>Index 278</p>
<p><b>Nicky Welton, Department of Social Medicine, University of Bristol</b><br />Dr Welton's research includes Bayesian statistical modeling in epidemiology and evidence synthesis and evidence consistency.</p> <p><b>Alex Sutton, Department of Health Sciences, University of Leicester</b><br />Dr Sutton, senior lecture in medical statistics, has a primary research interest in meta-analysis. This specifically includes methods to combine evidence from disparate sources, and methods to deal with the problem of publication bias. With numerous published papers in a variety of journals he has also collaborated on over 15 substantive evidence synthesis projects. He is lead author on one of the first textbooks on meta-analysis in medicine and is co-editor on a recently published Wiley book on publication bias.</p> <p><b>Nicola Cooper, Department of Health Sciences, University of Leicester</b><br />Dr Cooper’s primary research interest is in the interface and integration of medical statistics and health economics. This specifically includes methods for statistical modelling of cost data, integration of evidence synthesis within a decision-modelling context, handling of missing data in economic evaluations conducted alongside clinical trials, and the application of Bayesian statistical methods to all of the above.</p> <p><b>Keith Abrams, Department of Health Sciences, University of Leicester</b><br />Professor Abrams' research interests include the development and application of Bayesian methods in healthcare evaluation, systematic reviews and meta-analysis, and the joint modeling of longitudinal and time-to-event data. He has published dozens of articles in numerous international journals and is the co-author of two Wiley books in this area.</p> <p><b>Anthony E Ades, Department of Social Medicine, University of Bristol</b> with over 30 published articles in the last three years, Professor Ades' research interests include statistical methods for multi-parameter evidence synthesis in epidemiology, disease mapping and economic evaluation; Bayesian decision theory and the expected value of information; statistical and epidemiological methods in infectious disease surveillance.</p>
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. <p><i>Key features:</i></p> <ul type="disc"> <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>

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