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Visualization of Multi-indication Randomized Control Trial Evidence to Support Decision Making in Oncology: A Case
Sumayya Anwer1, Janharpreet Singh2, Sylwia Bujkiewicz2
1Centre for Reviews and Dissemination, University of York, York, UK.
Abstract:
BackgroundAs an increasing number of oncology drugs are licensed for multiple indications, sharing information across indications may help improve the precision of estimates for a target indication where evidence may be immature. Visualizing the accumulation of evidence and its characteristics across all indications can help inform policy makers as to whether multi-indication synthesis methods should be considered and guide expert elicitation on appropriate cross-indication assumptions.MethodsThe multi-indication oncology drug bevacizumab was selected as a case study. We used visualization methods including timeline, ridgeline, and split-violin plots to display evidence and synthesis results across 7 licensed cancer types, focusing on the evidence on overall and progression-free survival and the display of results from models with and without information sharing.ResultsThe proposed displays allow for visualization of key characteristics of the evidence to support the assessment of heterogeneity within and across indications and inform the feasibility of information-sharing models.LimitationsThe lack of consistent reporting of data in trial reports limits the visualization of some study characteristics. Tradeoffs between plot readability and the level of detail to include were required.ConclusionsClear graphical representations of the evolution and accumulation of evidence and synthesis results can provide a better understanding of the entire multi-indication evidence base, which can inform judgments regarding the appropriate use of data within and across indications. Interactive plots could help overcome some of the current limitations.ImplicationsThe proposed displays should be used to facilitate discussion with experts on the judgments required to assess the feasibility of using information-sharing methods to improve the estimation of relative treatment effects in evidence synthesis approaches and health technology assessment.HighlightsAn increasing number of oncology drugs are licensed for multiple indications; we developed visualization methods for multi-indication evidence that consider key characteristics unique to oncology.Graphical displays can be used to show the evolution of evidence within and across multiple indications.Clear evidence visualizations can be used as a tool to support evidence synthesis approaches, support policy makers, or guide expert elicitation.
Insights
Visualizing evidence across multiple cancer types helps assess oncology drug effectiveness. New graphical methods aid decision-making for multi-indication drug use and evidence synthesis.
Area of Science:
- Oncology
- Biostatistics
- Health Technology Assessment
Background:
- An increasing number of oncology drugs are approved for multiple indications.
- Sharing evidence across indications can improve treatment effect estimates, especially when data is limited.
Purpose of the Study:
- To develop and evaluate visualization methods for multi-indication oncology drug evidence.
- To inform policy makers and guide expert elicitation on cross-indication assumptions.
Main Methods:
- Used bevacizumab as a case study for a multi-indication oncology drug.
- Employed timeline, ridgeline, and split-violin plots to display evidence and synthesis results across 7 cancer types.
- Focused on overall survival, progression-free survival, and models with/without information sharing.
Main Results:
- The visualizations effectively display evidence characteristics, aiding assessment of within- and across-indication heterogeneity.
- The methods help evaluate the feasibility of information-sharing models for multi-indication drugs.
- Graphical representations clarify the evolution of evidence and synthesis outcomes.
Conclusions:
- Clear graphical displays enhance understanding of multi-indication evidence bases.
- These visualizations support judgments on appropriate data use within and across indications.
- Proposed displays facilitate expert discussions on information-sharing methods in evidence synthesis.
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