Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research

Cover
Title Page
Copyright Page
Contents
Acknowledgements
Chapter 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
Chapter 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
Part I Rationale, Planning, and Conduct
	Chapter 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
	Chapter 4 Running an IPD Meta-Analysis Project: From Developing the Protocol to Preparing Data for Meta-Analysis
		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
	Part I References
Part II Fundamental Statistical Methods and Principles
	Chapter 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
	Chapter 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
	Chapter 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
	Chapter 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
	Part II References
Part III Critical Appraisal and Dissemination
	Chapter 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
	Chapter 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
	Chapter 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
	Part III References
Part IV Special Topics in Statistics
	Chapter 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
	Chapter 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
	Chapter 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
	Part IV References
Part V Diagnosis, Prognosis and Prediction
	Chapter 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
	Chapter 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
	Chapter 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
	Chapter 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
	Part V References
Index
EULA