Individual Participant Data Meta-Analysis

1 Cover

2 Series Page

3 Title Page

4 Copyright Page

5 Dedication Page

6 Acknowledgements

7 1 Individual Participant Data Meta‐Analysis for Healthcare Research 1.1 Introduction 1.2 What Is IPD and How Does It Differ from Aggregate Data? 1.3 IPD Meta‐Analysis: A New Era for Evidence Synthesis 1.4 Scope of This Book and Intended Audience

8 Part I: Rationale, Planning, and Conduct 2 Rationale for Embarking on an IPD Meta‐Analysis Project 2.1 Introduction 2.2 How Does the Research Process Differ for IPD and Aggregate Data Meta‐Analysis Projects? 2.3 What Are the Potential Advantages of an IPD Meta‐Analysis Project? 2.4 What Are the Potential Challenges of an IPD Meta‐Analysis Project? 2.5 Empirical Evidence of Differences Between Results of IPD and Aggregate Data Meta‐Analysis Projects 2.6 Guidance for Deciding When IPD Meta‐Analysis Projects Are Needed to Evaluate Treatment Effects from Randomised Trials 2.7 Concluding Remarks 3 Planning and Initiating an IPD Meta‐Analysis Project 3.1 Introduction 3.2 Organisational Approach 3.3 Developing a Project Scope 3.4 Assessing Feasibility and ‘In Principle’ Support and Collaboration 3.5 Establishing a Team with the Right Skills 3.6 Advisory and Governance Functions 3.7 Estimating How Long the Project Will Take 3.8 Estimating the Resources Required 3.9 Obtaining Funding 3.10 Obtaining Ethical Approval 3.11 Data‐sharing Agreement 3.12 Additional Planning for Prospective Meta‐Analysis Projects 3.13 Concluding Remarks 4 Running an IPD Meta‐Analysis Project 4.1 Introduction 4.2 Preparing to Collect IPD 4.3 Initiating and Maintaining Collaboration 4.4 Obtaining IPD 4.5 Checking and Harmonising Incoming IPD 4.6 Checking the IPD to Inform Risk of Bias Assessments 4.7 Assessing and Presenting the Overall Quality of a Trial 4.8 Verification of Finalised Trial IPD 4.9 Merging IPD Ready for Meta‐Analysis 4.10 Concluding Remarks

9 Part I: References

10 Part II: Fundamental Statistical Methods and Principles 5 The Two‐stage Approach to IPD Meta‐Analysis 5.1 Introduction 5.2 First Stage of a Two‐stage IPD Meta‐Analysis 5.3 Second Stage of a Two‐stage IPD Meta‐Analysis 5.4 Meta‐regression and Subgroup Analyses 5.5 The ipdmetan Software Package 5.6 Combining IPD with Aggregate Data from non‐IPD Trials 5.7 Concluding Remarks 6 The One‐stage Approach to IPD Meta‐Analysis 6.1 Introduction 6.2 One‐stage IPD Meta‐Analysis Models Using Generalised Linear Mixed Models 6.3 One‐stage Models for Time‐to‐event Outcomes 6.4 One‐stage Models Combining Different Sources of Evidence 6.5 Reporting of One‐stage Models in Protocols and Publications 6.6 Concluding Remarks 7 Using IPD Meta‐Analysis to Examine Interactions between Treatment Effect and Participant‐level Covariates 7.1 Introduction 7.2 Meta‐regression and Its Limitations 7.3 Two‐stage IPD Meta‐Analysis to Estimate Treatment‐covariate Interactions 7.4 The One‐stage Approach 7.5 Combining IPD and non‐IPD Trials 7.6 Handling of Continuous Covariates 7.7 Handling of Categorical or Ordinal Covariates 7.8 Misconceptions and Cautions 7.9 Is My Identified Treatment‐covariate Interaction Genuine? 7.10 Reporting of Analyses of Treatment‐covariate Interactions 7.11 Can We Predict a New Patient’s Treatment Effect? 7.12 Concluding Remarks 8 One‐stage versus Two‐stage Approach to IPD Meta‐Analysis: Differences and Recommendations 8.1 Introduction 8.2 One‐stage and Two‐stage Approaches Usually Give Similar Results 8.3 Ten Key Reasons Why One‐stage and Two‐stage Approaches May Give Different Results 8.4 Recommendations and Guidance 8.5 Concluding Remarks

11 Part II: References

12 Part III: Critical Appraisal and Dissemination 9 Examining the Potential for Bias in IPD Meta‐Analysis Results 9.1 Introduction 9.2 Publication and Reporting Biases of Trials 9.3 Biased Availability of the IPD from Trials 9.4 Trial Quality (risk of bias) 9.5 Other Potential Biases Affecting IPD Meta‐Analysis Results 9.6 Concluding Remarks 10 Reporting and Dissemination of IPD Meta‐Analyses 10.1 Introduction 10.2 Reporting IPD Meta‐Analysis Projects in Academic Reports 10.3 Additional Means of Disseminating Findings 10.4 Concluding Remarks 11 A Tool for the Critical Appraisal of IPD Meta‐Analysis Projects (CheckMAP) 11.1 Introduction 11.2 The CheckMAP Tool 11.3 Was the IPD Meta‐Analysis Project Done within a Systematic Review Framework? 11.4 Were the IPD Meta‐Analysis Project Methods Pre‐specified in a Publicly Available Protocol? 11.5 Did the IPD Meta‐Analysis Project Have a Clear Research Question Qualified by Explicit Eligibility Criteria? 11.6 Did the IPD Meta‐Analysis Project Have a Systematic and Comprehensive Search Strategy? 11.7 Was the Approach to Data Collection Consistent and Thorough? 11.8 Were IPD Obtained from Most Eligible Trials and Their Participants? 11.9 Was the Validity of the IPD Checked for Each Trial? 11.10 Was the Risk of Bias Assessed for Each Trial and Its Associated IPD? 11.11 Were the Methods of Meta‐Analysis Appropriate? 11.12 Concluding Remarks

13 Part III: References

14 Part IV: Special Topics in Statistics 12 Power Calculations for Planning an IPD Meta‐Analysis 12.1 Introduction 12.2 Motivating Example: Power of a Planned IPD Meta‐Analysis of Trials of Interventions to Reduce Weight Gain in Pregnant Women 12.3 Power of an IPD Meta‐Analysis to Detect a Treatment‐covariate Interaction for a Continuous Outcome 12.4 The Contribution of Individual Trials Toward Power 12.5 The Impact of Model Assumptions on Power 12.6 Extensions 12.7 Concluding Remarks 13 Multivariate Meta‐Analysis Using IPD 13.1 Introduction 13.2 General Two‐stage Approach for Multivariate IPD Meta‐Analysis 13.3 Application to an IPD Meta‐Analysis of Anti‐hypertensive Trials 13.4 Extension to Multivariate Meta‐regression 13.5 Potential Limitations of Multivariate Meta‐Analysis 13.6 One‐stage Multivariate IPD Meta‐Analysis Applications 13.7 Special Applications of Multivariate Meta‐Analysis 13.8 Concluding Remarks 14 Network Meta‐Analysis Using IPD 14.1 Introduction 14.2 Rationale and Assumptions for Network Meta‐Analysis 14.3 Network Meta‐Analysis Models Assuming Consistency 14.4 Ranking Treatments 14.5 How Do We Examine Inconsistency between Direct and Indirect Evidence? 14.6 Benefits of IPD for Network Meta‐Analysis 14.7 Combining IPD and Aggregate Data in Network Meta‐Analysis 14.8 Further Topics 14.9 Concluding Remarks

15 Part IV: References

16 Part V: Diagnosis, Prognosis and Prediction 15 IPD Meta‐Analysis for Test Accuracy Research 15.1 Introduction 15.2 Motivating Example: Diagnosis of Fever in Children Using Ear Temperature 15.3 Key Steps Involved in an IPD Meta‐Analysis of Test Accuracy Studies 15.4 IPD Meta‐Analysis of Test Accuracy at Multiple Thresholds 15.5 IPD Meta‐Analysis for Examining a Test’s Clinical Utility 15.6 Comparing Tests 15.7 Concluding Remarks 16 IPD Meta‐Analysis for Prognostic Factor Research 16.1 Introduction 16.2 Potential Advantages of an IPD Meta‐Analysis 16.3 Key Steps Involved in an IPD Meta‐Analysis of Prognostic Factor Studies 16.4 Software 16.5 Concluding Remarks 17 IPD Meta‐Analysis for Clinical Prediction Model Research 17.1 Introduction 17.2 IPD Meta‐Analysis for Prediction Model Research 17.3 External Validation of an Existing Prediction Model Using IPD Meta‐Analysis 17.4 Updating and Tailoring of a Prediction Model Using IPD Meta‐Analysis 17.5 Comparison of Multiple Existing Prediction Models Using IPD Meta‐Analysis 17.6 Using IPD Meta‐Analysis to Examine the Added Value of a New Predictor to an Existing Prediction Model 17.7 Developing a New Prediction Model Using IPD Meta‐Analysis 17.8 Examining the Utility of a Prediction Model Using IPD Meta‐Analysis 17.9 Software 17.10 Reporting 17.11 Concluding Remarks 18 Dealing with Missing Data in an IPD Meta‐Analysis 18.1 Introduction 18.2 Motivating Example: IPD Meta‐Analysis Validating Prediction Models for Risk of Pre‐eclampsia in Pregnancy 18.3 Types of Missing Data in an IPD Meta‐Analysis 18.4 Recovering Actual Values of Missing Data within IPD 18.5 Mechanisms and Patterns of Missing Data in an IPD Meta‐Analysis 18.6 Multiple Imputation to Deal with Missing Data in a Single Study 18.7 Ensuring Congeniality of Imputation and Analysis Models 18.8 Dealing with Sporadically Missing Data in an IPD Meta‐Analysis by Applying Multiple Imputation for Each Study Separately 18.9 Dealing with Systematically Missing Data in an IPD Meta‐Analysis Using a Bivariate Meta‐Analysis of Partially and Fully Adjusted Results 18.10 Dealing with Both Sporadically and Systematically Missing Data in an IPD Meta‐Analysis Using Multilevel Modelling 18.11 Comparison of Methods and Recommendations 18.12 Software 18.13 Concluding Remarks