book

Network Meta-Analysis for Decision-Making

Name: Network Meta-Analysis for Decision-Making
ISBN: 9781118647509

by Sofia Dias, A. E. Ades, Nicky J. Welton, Jeroen P. Jansen, Alexander J. Sutton

March 2018

Intermediate to advanced

488 pages

15h 18m

English

Wiley

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Cover
Title Page
Preface
List of Abbreviations
About the Companion Website
1 Introduction to Evidence Synthesis
1.1 Introduction1.2 Why Indirect Comparisons and Network Meta-Analysis?1.3 Some Simple Methods1.4 An Example of a Network Meta-Analysis1.5 Assumptions Made by Indirect Comparisons and Network Meta-Analysis1.6 Which Trials to Include in a Network1.7 The Definition of Treatments and Outcomes: Network Connectivity1.8 Summary1.9 Exercises
2 The Core Model
2.1 Bayesian Meta-Analysis2.2 Development of the Core Models2.3 Technical Issues in Network Meta-Analysis2.4 Advantages of a Bayesian Approach2.5 Summary of Key Points and Further Reading2.6 Exercises
3 Model Fit, Model Comparison and Outlier Detection
3.1 Introduction3.2 Assessing Model Fit3.3 Model Comparison3.4 Outlier Detection in Network Meta-Analysis3.5 Summary and Further Reading3.6 Exercises
4 Generalised Linear Models
4.1 A Unified Framework for Evidence Synthesis4.2 The Generic Network Meta-Analysis Models4.3 Univariate Arm-Based Likelihoods4.4 Contrast-Based Likelihoods4.5 *Multinomial Likelihoods4.6 *Shared Parameter Models4.7 Choice of Prior Distributions4.8 Zero Cells4.9 Summary of Key Points and Further Reading4.10 Exercises
5 Network Meta-Analysis Within Cost-Effectiveness Analysis
5.1 Introduction5.2 Sources of Evidence for Relative Treatment Effects and the Baseline Model5.3 The Baseline Model5.4 The Natural History Model5.5 Model Validation and Calibration Through Multi-Parameter Synthesis5.6 Generating the Outputs Required for Cost-Effectiveness Analysis5.7 Strategies to Implement Cost-Effectiveness Analyses5.8 Summary and Further Reading5.9 Exercises

6 Adverse Events and Other Sparse Outcome Data
6.1 Introduction6.2 Challenges Regarding the Analysis of Sparse Data in Pairwise and Network Meta-Analysis6.3 Strategies to Improve the Robustness of Estimation of Effects from Sparse Data in Network Meta-Analysis6.4 Summary and Further Reading6.5 Exercises
7 Checking for Inconsistency
7.1 Introduction7.2 Network Structure7.3 Loop Specific Tests for Inconsistency7.4 A Global Test for Loop Inconsistency7.5 Response to Inconsistency7.6 The Relationship between Heterogeneity and Inconsistency7.7 Summary and Further Reading7.8 Exercises
8 Meta-Regression for Relative Treatment Effects
8.1 Introduction8.2 Basic Concepts8.3 Heterogeneity, Meta-Regression and Predictive Distributions8.4 Meta-Regression Models for Network Meta-Analysis8.5 Individual Patient Data in Meta-Regression8.6 Models with Treatment-Level Covariates8.7 Implications of Meta-Regression for Decision Making8.8 Summary and Further Reading8.9 Exercises
9 Bias Adjustment Methods
9.1 Introduction9.2 Adjustment for Bias Based on Meta-Epidemiological Data9.3 Estimation and Adjustment for Bias in Networks of Trials9.4 Elicitation of Internal and External Bias Distributions from Experts9.5 Summary and Further Reading9.6 Exercises
10 *Network Meta-Analysis of Survival Outcomes
10.1 Introduction10.2 Time-to-Event Data10.3 Parametric Survival Functions10.4 The Relative Treatment Effect10.5 Network Meta-Analysis of a Single Effect Measure per Study10.6 Network Meta-Analysis with Multivariate Treatment Effects10.7 Data and Likelihood10.8 Model Choice10.9 Presentation of Results10.10 Illustrative Example10.11 Network Meta-Analysis of Survival Outcomes for Cost-Effectiveness Evaluations10.12 Summary and Further Reading10.13 Exercises
11 *Multiple Outcomes
11.1 Introduction11.2 Multivariate Random Effects Meta-Analysis11.3 Multinomial Likelihoods and Extensions of Univariate Methods11.4 Chains of Evidence11.5 Follow-Up to Multiple Time Points: Gastro-Esophageal Reflux Disease11.6 Multiple Outcomes Reported in Different Ways: Influenza11.7 Simultaneous Mapping and Synthesis11.8 Related Outcomes Reported in Different Ways: Advanced Breast Cancer11.9 Repeat Observations for Continuous Outcomes: Fractional Polynomials11.10 Synthesis for Markov Models11.11 Summary and Further Reading11.12 Exercises
12 Validity of Network Meta-Analysis
12.1 Introduction12.2 What Are the Assumptions of Network Meta-Analysis?12.3 Direct and Indirect Comparisons: Some Thought Experiments12.4 Empirical Studies of the Consistency Assumption12.5 Quality of Evidence Versus Reliability of Recommendation12.6 Summary and Further Reading12.7 Exercises
Solutions to Exercises
Appendices
References
Index
End User License Agreement

Overview

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.

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