Visualisation of multi-indication randomised control trial evidence to support decision-making in oncology: a case study on bevacizumab

TL;DR

This paper presents visualisation methods for summarising and comparing evidence across multiple indications of oncology drugs, exemplified by bevacizumab, to aid decision-making and evidence synthesis.

Contribution

It introduces novel visualisation techniques for multi-indication evidence maps, enabling dynamic comparison of evidence across different cancer types.

Findings

Visual evidence maps reveal patterns in study data over time.

Tools facilitate updating evidence as new data emerge.

Visualisations highlight gaps and consistencies in trial reporting.

Abstract

Background: Evidence maps have been used in healthcare to understand existing evidence and to support decision-making. In oncology they have been used to summarise evidence within a disease area but have not been used to compare evidence across different diseases. As an increasing number of oncology drugs are licensed for multiple indications, visualising the accumulation of evidence across all indications can help inform policy-makers, support evidence synthesis approaches, or to guide expert elicitation on appropriate cross-indication assumptions. Methods: The multi-indication oncology therapy bevacizumab was selected as a case-study. We used visualisation methods including timeline, ridgeline and split-violin plots to display evidence across seven licensed cancer types, focusing on the evolution of evidence on overall and progression-free survival over time as well as the quality of the evidence available. Results: Evidence maps for bevacizumab allow for visualisation of patterns in study-level evidence, which can be updated as evidence accumulates over time. The developed tools display the observed data and synthesised evidence across- and within-indications. Limitations: The effectiveness of the plots produced are limited by the lack of complete and consistent reporting of evidence in trial reports. Trade-offs were necessary when deciding the level of detail that could be shown while keeping the plots coherent. Conclusions: Clear graphical representations of the evolution and accumulation of evidence can provide a better understanding of the entire evidence base which can inform judgements regarding the appropriate use of data within and across indications. Implications: Improved visualisations of evidence can help the development of multi-indication evidence synthesis. The proposed evidence displays can lead to the efficient use of information for health technology assessment.