Basic Data Structure for Hierarchical Composite Endpoints: An Application to Kidney Disease Trials

Abstract

Introduction: Hierarchical composite endpoints (HCE), including the recently introduced kidney HCE, are complex endpoints that are usually analyzed by win statistics and are visualized using novel maraca plots. As a result of its novelty and the complexity of the analyses of HCE using win statistics, the construction of analysis datasets that conform to the fundamental principles put forward by the Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) is not straightforward.Objectives: We show that in the case of a fixed follow-up it is possible to construct an analysis dataset including win statistics generation and visualization of HCE using maraca plots.Methods: We use theoretical justification for the fixed follow-up designs to show that the pair-wise comparisons of participants for the win statistics analyses can be reduced to a participant-level ranking, and use the fundamental principles put forward by CDISC and Tidy principles of the data science community to derive an ADaM-compliant dataset.Results: In the setting of fixed follow-up designs, we construct an ADaM-complaint dataset fro conducting win statistics analyses and visualization using maraca plots, with the required metadata traceability.Conclusions: Based on the growing importance of HCEs in clinical trials, and the difficulty in creating ADaM-compliant datasets for these analyses, we provide the principles to create such datasets, to prompt the clinical community and CDISC to work towards standardization of analysis datasets for hierarchical composite endpoints.