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Network Meta-Analysis for Decision-Making

Book Description

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.

Table of Contents

  1. Cover
  2. Title Page
  3. Preface
  4. List of Abbreviations
  5. About the Companion Website
  6. 1 Introduction to Evidence Synthesis
    1. 1.1 Introduction
    2. 1.2 Why Indirect Comparisons and Network Meta-Analysis?
    3. 1.3 Some Simple Methods
    4. 1.4 An Example of a Network Meta-Analysis
    5. 1.5 Assumptions Made by Indirect Comparisons and Network Meta-Analysis
    6. 1.6 Which Trials to Include in a Network
    7. 1.7 The Definition of Treatments and Outcomes: Network Connectivity
    8. 1.8 Summary
    9. 1.9 Exercises
  7. 2 The Core Model
    1. 2.1 Bayesian Meta-Analysis
    2. 2.2 Development of the Core Models
    3. 2.3 Technical Issues in Network Meta-Analysis
    4. 2.4 Advantages of a Bayesian Approach
    5. 2.5 Summary of Key Points and Further Reading
    6. 2.6 Exercises
  8. 3 Model Fit, Model Comparison and Outlier Detection
    1. 3.1 Introduction
    2. 3.2 Assessing Model Fit
    3. 3.3 Model Comparison
    4. 3.4 Outlier Detection in Network Meta-Analysis
    5. 3.5 Summary and Further Reading
    6. 3.6 Exercises
  9. 4 Generalised Linear Models
    1. 4.1 A Unified Framework for Evidence Synthesis
    2. 4.2 The Generic Network Meta-Analysis Models
    3. 4.3 Univariate Arm-Based Likelihoods
    4. 4.4 Contrast-Based Likelihoods
    5. 4.5 *Multinomial Likelihoods
    6. 4.6 *Shared Parameter Models
    7. 4.7 Choice of Prior Distributions
    8. 4.8 Zero Cells
    9. 4.9 Summary of Key Points and Further Reading
    10. 4.10 Exercises
  10. 5 Network Meta-Analysis Within Cost-Effectiveness Analysis
    1. 5.1 Introduction
    2. 5.2 Sources of Evidence for Relative Treatment Effects and the Baseline Model
    3. 5.3 The Baseline Model
    4. 5.4 The Natural History Model
    5. 5.5 Model Validation and Calibration Through Multi-Parameter Synthesis
    6. 5.6 Generating the Outputs Required for Cost-Effectiveness Analysis
    7. 5.7 Strategies to Implement Cost-Effectiveness Analyses
    8. 5.8 Summary and Further Reading
    9. 5.9 Exercises
  11. 6 Adverse Events and Other Sparse Outcome Data
    1. 6.1 Introduction
    2. 6.2 Challenges Regarding the Analysis of Sparse Data in Pairwise and Network Meta-Analysis
    3. 6.3 Strategies to Improve the Robustness of Estimation of Effects from Sparse Data in Network Meta-Analysis
    4. 6.4 Summary and Further Reading
    5. 6.5 Exercises
  12. 7 Checking for Inconsistency
    1. 7.1 Introduction
    2. 7.2 Network Structure
    3. 7.3 Loop Specific Tests for Inconsistency
    4. 7.4 A Global Test for Loop Inconsistency
    5. 7.5 Response to Inconsistency
    6. 7.6 The Relationship between Heterogeneity and Inconsistency
    7. 7.7 Summary and Further Reading
    8. 7.8 Exercises
  13. 8 Meta-Regression for Relative Treatment Effects
    1. 8.1 Introduction
    2. 8.2 Basic Concepts
    3. 8.3 Heterogeneity, Meta-Regression and Predictive Distributions
    4. 8.4 Meta-Regression Models for Network Meta-Analysis
    5. 8.5 Individual Patient Data in Meta-Regression
    6. 8.6 Models with Treatment-Level Covariates
    7. 8.7 Implications of Meta-Regression for Decision Making
    8. 8.8 Summary and Further Reading
    9. 8.9 Exercises
  14. 9 Bias Adjustment Methods
    1. 9.1 Introduction
    2. 9.2 Adjustment for Bias Based on Meta-Epidemiological Data
    3. 9.3 Estimation and Adjustment for Bias in Networks of Trials
    4. 9.4 Elicitation of Internal and External Bias Distributions from Experts
    5. 9.5 Summary and Further Reading
    6. 9.6 Exercises
  15. 10 *Network Meta-Analysis of Survival Outcomes
    1. 10.1 Introduction
    2. 10.2 Time-to-Event Data
    3. 10.3 Parametric Survival Functions
    4. 10.4 The Relative Treatment Effect
    5. 10.5 Network Meta-Analysis of a Single Effect Measure per Study
    6. 10.6 Network Meta-Analysis with Multivariate Treatment Effects
    7. 10.7 Data and Likelihood
    8. 10.8 Model Choice
    9. 10.9 Presentation of Results
    10. 10.10 Illustrative Example
    11. 10.11 Network Meta-Analysis of Survival Outcomes for Cost-Effectiveness Evaluations
    12. 10.12 Summary and Further Reading
    13. 10.13 Exercises
  16. 11 *Multiple Outcomes
    1. 11.1 Introduction
    2. 11.2 Multivariate Random Effects Meta-Analysis
    3. 11.3 Multinomial Likelihoods and Extensions of Univariate Methods
    4. 11.4 Chains of Evidence
    5. 11.5 Follow-Up to Multiple Time Points: Gastro-Esophageal Reflux Disease
    6. 11.6 Multiple Outcomes Reported in Different Ways: Influenza
    7. 11.7 Simultaneous Mapping and Synthesis
    8. 11.8 Related Outcomes Reported in Different Ways: Advanced Breast Cancer
    9. 11.9 Repeat Observations for Continuous Outcomes: Fractional Polynomials
    10. 11.10 Synthesis for Markov Models
    11. 11.11 Summary and Further Reading
    12. 11.12 Exercises
  17. 12 Validity of Network Meta-Analysis
    1. 12.1 Introduction
    2. 12.2 What Are the Assumptions of Network Meta-Analysis?
    3. 12.3 Direct and Indirect Comparisons: Some Thought Experiments
    4. 12.4 Empirical Studies of the Consistency Assumption
    5. 12.5 Quality of Evidence Versus Reliability of Recommendation
    6. 12.6 Summary and Further Reading
    7. 12.7 Exercises
  18. Solutions to Exercises
  19. Appendices
  20. References
  21. Index
  22. End User License Agreement