Individual participant data meta-analysis : a handbook for healthcare research / edited by Richard D. Riley, Jayne F. Tierney, Lesley A. Stewart.

Format:

Book

Contributor:

Tierney, Jayne F., editor.

Stewart, Lesley A., editor.

Riley, Richard D., editor.

Series:

Statistics in practice.

Statistics in Practice

Language:

English

German

Subjects (All):

Medicine--Research--Statistical methods.

Medicine.

Physical Description:

1 online resource (563 pages)

Place of Publication:

Hoboken, New Jersey : Wiley, [2021]

Summary:

"This book is a pivotal textbook for those considering, undertaking or appraising an evidence synthesis based on IPD for clinical research, especially those interested in intervention effects, modifiers of treatment response, identification of risk or prognostic factors, and the development and validation of risk prediction models. It covers all the key concepts and stages of a systematic review and meta analysis of IPD, focusing primarily on the synthesis of randomised trials, as well as specialist topics, such as risk prediction, observational studies and advanced statistical methods. The book offers nontechnical and practical examples, summary and learning points, and guidance including reporting criteria, software demonstrations, and illustrated applications. Describing the key features of the approach, this book will enable the reader to: Understand when the IPD approach is needed How to undertake the systematic review and identify relevant evidence How to obtain, check and manage the IPD How to minimise potential biases and clearly report methods and results How to make the best use of IPD meta-analyses to inform policy, practice and research. Case-study chapters are also provided, where the trials and tribulations of undertaking IPD projects of randomised trials and observational studies are described by researchers in a range of clinical fields"-- Provided by publisher.

Contents:

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.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781119333753

111933375X

9781119333760

1119333768

9781119333784

1119333784

OCLC:

1255233548