Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points.
Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data.
Intended for a broad audience, the book will enable the reader to:
Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review Recognise the scope, resources and challenges of IPD meta-analysis projects Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators Understand how to obtain, check, manage and harmonise IPD from multiple studies Examine risk of bias (quality) of IPD and minimise potential biases throughout the project Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research Critically appraise existing IPD meta-analysis projects Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models
Detailed examples and case studies are provided throughout.
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 2.3 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 2.7 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 3.3 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 4.3 Initiating and Maintaining Collaboration 57 4.4 Obtaining IPD 59 4.5 Checking and Harmonising Incoming IPD 62 4.6 Checking the IPD to Inform Risk of Bias Assessments 66 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 5.3 Second Stage of a Two-stage IPD Meta-analysis 106 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 6.3 One-stage Models for Time-to-event Outcomes 152 6.4 One-stage Models Combining Different Sources of Evidence 159 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 7.3 Two-stage IPD Meta-analysis to Estimate Treatment-covariate Interactions 168 7.4 The One-stage Approach 174 7.5 Combining IPD and non-IPD Trials 181 7.6 Handling of Continuous Covariates 184 7.7 Handling of Categorical or Ordinal Covariates 191 7.8 Misconceptions and Cautions 191 7.9 Is My Identified Treatment-covariate Interaction Genuine? 195 7.10 Reporting of Analyses of Treatment-covariate Interactions 196 7.11 Can We Predict a New Patient’s Treatment Effect? 196 7.11.1 Linking Predictions to Clinical Decision Making 198 7.12 Concluding Remarks 198 8 One-stage versus Two-stage Approach to IPD Meta-analysis: Differences and Recommendations 199 Richard D. Riley, Danielle L. Burke, and Tim Morris 8.1 Introduction 200 8.2 One-stage and Two-stage Approaches Usually Give Similar Results 200 8.3 Ten Key Reasons Why One-stage and Two-stage Approaches May Give Different Results 203 8.4 Recommendations and Guidance 216 8.5 Concluding Remarks 217 Part II References 219 Part III Critical Appraisal and Dissemination 237 9 Examining the Potential for Bias in IPD Meta-analysis Results 239 Richard D. Riley, Jayne F. Tierney, and Lesley A. Stewart 9.1 Introduction 240 9.2 Publication and Reporting Biases of Trials 240 9.3 Biased Availability of the IPD from Trials 244 9.4 Trial Quality (risk of bias) 247 9.5 Other Potential Biases Affecting IPD Meta-analysis Results 248 9.6 Concluding Remarks 251 10 Reporting and Dissemination of IPD Meta-analyses 253 Lesley A. Stewart, Richard D. Riley, and Jayne F. Tierney 10.1 Introduction 253 10.2 Reporting IPD Meta-analysis Projects in Academic Reports 254 10.3 Additional Means of Disseminating Findings 266 10.4 Concluding Remarks 270 11 A Tool for the Critical Appraisal of IPD Meta-analysis Projects (CheckMAP) 271 Jayne F. Tierney, Lesley A. Stewart, Claire L. Vale, and Richard D. Riley 11.1 Introduction 271 11.2 The CheckMAP Tool 272 11.3 Was the IPD Meta-analysis Project Done within a Systematic Review Framework? 272 11.4 Were the IPD Meta-analysis Project Methods Pre-specified in a Publicly Available Protocol? 274 11.5 Did the IPD Meta-analysis Project Have a Clear Research Question Qualified by Explicit Eligibility Criteria? 276 11.6 Did the IPD Meta-analysis Project Have a Systematic and Comprehensive Search Strategy? 276 11.7 Was the Approach to Data Collection Consistent and Thorough? 277 11.8 Were IPD Obtained from Most Eligible Trials and Their Participants? 277 11.9 Was the Validity of the IPD Checked for Each Trial? 278 11.10 Was the Risk of Bias Assessed for Each Trial and Its Associated IPD? 27811.10.1 Was the Randomisation Process Checked Based on IPD? 278 11.11 Were the Methods of Meta-analysis Appropriate? 280 11.12 Concluding Remarks 283 Part III References 285 Part IV Special Topics in Statistics 291 12 Power Calculations for Planning an IPD Meta-analysis 293 Richard D. Riley and Joie Ensor 12.1 Introduction 294 12.2 Motivating Example: Power of a Planned IPD Meta-analysis of Trials of Interventions to Reduce Weight Gain in Pregnant Women 295 12.3 The Contribution of Individual Trials Toward Power 301 12.4 The Impact of Model Assumptions on Power 302 12.5 Extensions 305 12.6 Concluding Remarks 309 13 Multivariate Meta-analysis Using IPD 311 Richard D. Riley, Dan Jackson, and Ian R. White 13.1 Introduction 312 13.2 General Two-stage Approach for Multivariate IPD Meta-analysis 314 13.3 Application to an IPD Meta-analysis of Anti-hypertensive Trials 329 13.4 Extension to Multivariate Meta-regression 333 13.5 Potential Limitations of Multivariate Meta-analysis 334 13.6 One-stage Multivariate IPD Meta-analysis Applications 337 13.7 Special Applications of Multivariate Meta-analysis 340 13.8 Concluding Remarks 346 14 Network Meta-analysis Using IPD 347 Richard D. Riley, David M. Phillippo, and Sofia Dias 14.1 Introduction 348 14.2 Rationale and Assumptions for Network Meta-analysis 348 14.3 Network Meta-analysis Models Assuming Consistency 350 14.4 Ranking Treatments 357 14.5 How Do We Examine Inconsistency between Direct and Indirect Evidence? 359 14.6 Benefits of IPD for Network Meta-analysis 361 14.7 Combining IPD and Aggregate Data in Network Meta-analysis 365 14.8 Further Topics 370 14.9 Concluding Remarks 372 Part IV References 375 Part V Diagnosis, Prognosis and Prediction 387 15 IPD Meta-analysis for Test Accuracy Research 389 Richard D. Riley, Brooke Levis, and Yemisi Takwoingi 389 15.1 Introduction 390 15.2 Motivating Example: Diagnosis of Fever in Children Using Ear Temperature 394 15.3 Key Steps Involved in an IPD Meta-analysis of Test Accuracy Studies 397 15.4 IPD Meta-analysis of Test Accuracy at Multiple Thresholds 410 15.5 IPD Meta-analysis for Examining a Test’s Clinical Utility 414 15.6 Comparing Tests 418 15.7 Concluding Remarks 420 16 IPD Meta-analysis for Prognostic Factor Research 421 Richard D. Riley, Karel G.M. Moons, and Thomas P.A. Debray 16.1 Introduction 422 16.2 Potential Advantages of an IPD Meta-analysis 424 16.3 Key Steps Involved in an IPD Meta-analysis of Prognostic Factor Studies 427 16.4 Software 444 16.5 Concluding Remarks 444 17 IPD Meta-analysis for Clinical Prediction Model Research 447 Richard D. Riley, Kym I.E. Snell, Laure Wynants, Valentijn M.T. de Jong, Karel G.M. Moons, and Thomas P.A. Debray 17.1 Introduction 448 17.2 IPD Meta-analysis for Prediction Model Research 448 17.3 External Validation of an Existing Prediction Model Using IPD Meta-analysis 455 17.4 Updating and Tailoring of a Prediction Model Using IPD Meta-analysis 470 17.5 Comparison of Multiple Existing Prediction Models Using IPD Meta-analysis 472 17.6 Using IPD Meta-analysis to Examine the Added Value of a New Predictor to an Existing Prediction Model 478 17.7 Developing a New Prediction Model Using IPD Meta-analysis 479 17.8 Examining the Utility of a Prediction Model Using IPD Meta-analysis 491 17.9 Software 494 17.10 Reporting 495 17.11 Concluding Remarks 495 18 Dealing with Missing Data in an IPD Meta-analysis 499 Thomas Debray, Kym I.E. Snell, Matteo Quartagno, Shahab Jolani, Karel G.M. Moons, and Richard D. Riley 499 18.1 Introduction 500 18.2 Motivating Example: IPD Meta-analysis Validating Prediction Models for Risk of Preeclampsia in Pregnancy 500 18.3 Types of Missing Data in an IPD Meta-analysis 502 18.4 Recovering Actual Values of Missing Data within IPD 502 18.5 Mechanisms and Patterns of Missing Data in an IPD Meta-analysis 502 18.6 Multiple Imputation to Deal with Missing Data in a Single Study 506 18.7 Ensuring Congeniality of Imputation and Analysis Models 509 18.8 Dealing with Sporadically Missing Data in an IPD Meta-analysis by Applying Multiple Imputation for Each Study Separately 509 18.9 Dealing with Systematically Missing Data in an IPD Meta-analysis Using a Bivariate Metaanalysis of Partially and Fully Adjusted Results 511 18.10 Dealing with Both Sporadically and Systematically Missing Data in an IPD Meta-analysis Using Multilevel Modelling 514 18.11 Comparison of Methods and Recommendations 521 18.12 Software 523 18.13 Concluding Remarks 524 Part V References 525 Index 000
Richard D. Riley is Professor of Biostatistics in the School of Medicine, Keele University, UK. Jayne F. Tierney is Professor of Evidence Synthesis at the MRC Clinical Trials Unit, University College London, UK. Lesley A. Stewart is Professor of Evidence Synthesis and Director of the Centre for Reviews and Dissemination, University of York, UK.
