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
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
18 Index
List of Tables
1 Chapter 2Table 2.1 Key potential advantages of an IPD meta‐analysis project compared w...Table 2.2 Signalling questions to help decide when aggregate data are insuffi...
2 Chapter 3Table 3.1 Consent sought to collaborate in an IPD analysis of predictive fact...
3 Chapter 4Table 4.1 Excerpt from a data dictionary developed for an IPD meta‐analysis o...Table 4.2 Excerpt from a data dictionary developed for an IPD meta‐analysis p...Table 4.3 Example of items to include in a data transfer guide when requestin...
