Acknowledgements xxiii 1 Individual Participant Data Meta-analysis for Healthcare Research 1 Richard D. Riley, Lesley A. Stewart, and Jayne F. Tierney 1.1 Introduction 1 1.2 What Is IPD and How Does It Differ from Aggregate Data? 1 1.3 IPD Meta-analysis: A New Era for Evidence Synthesis 2 1.4 Scope of This Book and Intended Audience 2 Part I Rationale, Planning, and Conduct 7 2 Rationale for Embarking on an IPD Meta-analysis Project 9 Jayne F.

Tierney, Richard D. Riley, Catrin Tudur Smith, Mike Clarke, and Lesley A. Stewart 2.1 Introduction 9 2.2 How Does the Research Process Differ for IPD and Aggregate Data Meta-analysis Projects? 10 2.2.1 The Research Aims 10 2.2.

2 The process and methods 10 2.3 What Are the Potential Advantages of an IPD Meta-analysis Project? 11 2.4 What Are the Potential Challenges of an IPD Meta-Analysis Project? 14 2.5 Empirical Evidence of Differences between Results of IPD and Aggregate Data Metaanalysis Projects 14 2.6 Guidance for Deciding When IPD Meta-analysis Projects Are Needed to Evaluate Treatment Effects from Randomised Trials 15 2.6.1 Are IPD Needed to Tackle the Research Question? 15 2.6.

2 Are IPD Needed to Improve the Completeness and Uniformity of Outcomes and Participant-level Covariates? 17 2.6.3 Are IPD Needed to Improve the Information Size? 17 2.6.4 Are IPD Needed to Improve the Quality of Analysis? 18 2.7 Concluding Remarks 19 3 Planning and Initiating an IPD Meta-analysis Project 21 Lesley A. Stewart, Richard D. Riley, and Jayne F.

Tierney 3.1 Introduction 22 3.2 Organisational Approach 22 3.2.1 Collaborative IPD Meta-analysis Project 22 3.2.2 IPD Meta-analysis Projects Using Data Repositories or Data-sharing Platforms 24 3.3 Developing a Project Scope 26 3.

4 Assessing Feasibility and ''In Principle'' Support and Collaboration 26 3.5 Establishing a Team with the Right Skills 29 3.6 Advisory and Governance Functions 30 3.7 Estimating How Long the Project Will Take 31 3.8 Estimating the Resources Required 33 3.9 Obtaining Funding 38 3.10 Obtaining Ethical Approval 39 3.11 Data-sharing Agreement 41 3.

12 Additional Planning for Prospective Meta-analysis Projects 41 3.13 Concluding Remarks 43 4 Running an IPD Meta-analysis Project: From Developing the Protocol to Preparing Data for Meta-analysis 45 Jayne F. Tierney, Richard D. Riley, Larysa H.M. Rydzewska, and Lesley A. Stewart 4.1 Introduction 46 4.

2 Preparing to Collect IPD 46 4.2.1 Defining the Objectives and Eligibility Criteria 46 4.2.2 Developing the Protocol for an IPD Meta-analysis Project 49 4.2.3 Identifying and Screening Potentially Eligible Trials 51 4.2.

4 Deciding Which Information Is Needed to Summarise Trial Characteristics 51 4.2.5 Deciding How Much IPD Are Needed 52 4.2.6 Deciding Which Variables Are Needed in the IPD 52 4.2.7 Developing a Data Dictionary for the IPD 55 4.3 Initiating and Maintaining Collaboration 57 4.

4 Obtaining IPD 59 4.4.1 Ensuring That IPD Are De-identified 59 4.4.2 Providing Data Transfer Guidance 60 4.4.3 Transferring trial IPD securely 61 4.4.

4 Storing Trial IPD Securely 61 4.4.5 Making Best Use of IPD from Repositories 61 4.5 Checking and Harmonising Incoming IPD 62 4.5.1 The Process and Principles 63 4.5.2 Initial Checking of IPD for Each Trial 63 4.

5.3 Harmonising IPD across Trials 64 4.5.4 Checking the Validity, Range and Consistency of Variables 65 4.6 Checking the IPD to Inform Risk of Bias Assessments 66 4.6.1 The Randomisation Process 68 4.6.

2 Deviations from the Intended Interventions 71 4.6.3 Missing Outcome Data 73 4.6.4 Measurement of the Outcome 74 4.7 Assessing and Presenting the Overall Quality of a Trial 76 4.8 Verification of Finalised Trial IPD 77 4.9 Merging IPD Ready for Meta-analysis 77 4.

10 Concluding Remarks 80 Part I References 81 Part II Fundamental Statistical Methods and Principles 87 5 The Two-stage Approach to IPD Meta-analysis 89 Richard D. Riley, Thomas P.A. Debray, Tim P. Morris, and Dan Jackson 5.1 Introduction 90 5.2 First Stage of a Two-stage IPD Meta-analysis 90 5.2.

1 General Format of Regression Models to Use in the First Stage 92 5.2.2 Estimation of Regression Models Applied in the First Stage 92 5.2.3 Regression for Different Outcome Types 94 5.2.3.1 Continuous Outcomes 94 5.

2.3.2 Binary Outcomes 98 5.2.3.3 Ordinal and Multinomial Outcomes 99 5.2.3.

4 Count and Incidence Rate Outcomes 100 5.2.3.5 Time-to-Event Outcomes 101 5.2.4 Adjustment for Prognostic Factors 102 5.2.5 Dealing with Other Trial Designs and Missing Data 103 5.

3 Second Stage of a Two-stage IPD Meta-analysis 106 5.3.1 Meta-analysis Assuming a Common Treatment Effect 106 5.3.2 Meta-analysis Assuming Random Treatment Effects 107 5.3.3 Forest Plots and Percentage Trial Weights 110 5.3.

4 Heterogeneity Measures and Statistics 110 5.3.5 Alternative Weighting Schemes 112 5.3.6 Frequentist Estimation of the Between-Trial Variance of Treatment Effect 113 5.3.7 Deriving Confidence Intervals for the Summary Treatment Effect 113 5.3.

8 Bayesian Estimation Approaches 115 5.3.8.1 An Introduction to Bayes'' Theorem and Bayesian Inference 115 5.3.8.2 Using a Bayesian Meta-Analysis Model in the Second Stage 115 5.3.

8.3 Applied Example 117 5.3.9 Interpretation of Summary Effects from Meta-analysis 118 5.3.10 Prediction Interval for the Treatment Effect in a New Trial 118 5.4 Meta-regression and Subgroup Analyses 120 5.5 The ipdmetan Software Package 121 5.

6 Combining IPD with Aggregate Data from non-IPD Trials 124 5.7 Concluding Remarks 125 6 The One-stage Approach to IPD Meta-analysis 127 Richard D. Riley and Thomas P.A. Debray 127 6.1 Introduction 128 6.2 One-stage IPD Meta-analysis Models Using Generalised Linear Mixed Models 129 6.2.

1 Basic Statistical Framework of One-stage Models Using GLMMs 129 6.2.1.1 Continuous Outcomes 130 6.2.1.2 Binary Outcomes 130 6.2.

1.3 Ordinal and Multinomial Outcomes 135 6.2.1.4 Count and Incidence Rate Outcomes 136 6.2.2 Specifying Parameters as Either Common, Stratified, or Random 136 6.2.

3 Accounting for Clustering of Participants within Trials 139 6.2.3.1 Examples 141 6.2.4 Choice of Stratified Intercept or Random Intercepts 141 6.2.4.

1 Findings from Simulation Studies 142 6.2.4.2 Our Preference for Using a Stratified Intercept 142 6.2.4.3 Allowing for Correlation between Random Effects on Intercept and Treatment Effect 143 6.2.

5 Stratified or Common Residual Variances 144 6.2.6 Adjustment for Prognostic Factors 145 6.2.7 Inclusion of Trial-level Covariates 145 6.2.8 Estimation of One-stage IPD Meta-analysis Models Using GLMMs 146 6.2.

8.1 Software for Fitting One-stage Models 146 6.2.8.2 ML Estimation and Downward Bias in Between-trial Variance Estimates 146 6.2.8.3 Trial-specific Centering of Variables to Improve ML Estimation of One-stage Models with a Stratified Intercept 147 6.

2.8.4 REML Estimation 147 6.2.8.5 Deriving Confidence Intervals for ParametersPpost-estimation 149 6.2.8.

6 Prediction Intervals 151 6.2.8.7 Derivation of Percentage Trial Weights 151 6.2.8.8 Bayesian Estimation for One-stage Models 151 6.2.

9 A Summary of Recommendations 152 6.3 One-stage Models for Time-to-event Outcomes 152 6.3.1 Cox Proportional Hazard Framework 152 6.3.1.1 Stratifying Using Proportional Baseline Hazards and Frailty Models 152 6.3.

1.2 Stratifying Baseline Hazards without Assuming Proportionality 154 6.3.1.3 Comparison of Approaches 154 6.3.1.4 Estimation Methods 154 6.

3.1.5 Example 156 6.3.2 Fully Parametric Approaches 157 6.3.3 Extension to Time-varying Hazard Ratios and Joint Models 157 6.4 One-stage Models Combining Different Sources of Evidence 159 6.

4.1 Combining IPD Trials with Partially Reconstructed IPD from Non-IPD Trials 159 6.4.2 Combining IPD and Aggregate Data Using Hierarchical Related Regression 160 6.4.3 Combining IPD from Parallel Group, Cluster and Cross-over Trials 161 6.5 Reporting of One-stage Models in Protocols and Publications 162 6.6 Concluding Remarks 162 7 Using IPD Meta-analysis to Examine Interactions between Treatment Effect and Participant-level Covariates 163 Richard D.

Riley and David J. Fisher 7.1 Introduction 164 7.2 Meta-regression and Its Limitations 166 7.2.1 Meta-regression of Aggregated Participant-level Covariates 166 7.2.2 Low Power and Aggregation Bias 166 7.

2.3 Empirical Evidence of the Difference Between Using Across-trial and Within-trial Information to Estimate Treatment-covariate Interactions 167 7.3 Two-stage IPD Meta-analysis to Estimate Treatment-covariate Interactions 168 7.3.1 The Two-stage Approach 168 7.3.2 Applied Example: Is the Effect of Anti-hypertensive Treatment Different for Males and Females? 170 7.3.

3 Do Not Quantify Interactions by Comparing Meta-analysis Results for Subgroups 171 7.4 The One-stage Approach 1.