Assessing neuro-oncology clinical trial impact and value: Testing a novel multi-criteria decision analysis app

J Clin Neurosci. 2023 Dec:118:70-78. doi: 10.1016/j.jocn.2023.07.024. Epub 2023 Oct 25.

Abstract

Background: Many clinical trials are conducted globally, creating challenges in deciding which trial outcomes deserve a clinician's focus and where to direct limited resources. Determining the 'value' of a clinical trial relative to others could be useful in this context. The aim of this study was to test a novel web-based application using multi-criteria decision analysis (MCDA) to rank clinical trial value.

Methods: The MCDA tool combines seven metrics: unmet need; target population size; access; outcomes; cost; academic impact and use of results. Clinical trials were ranked according to their calculated 'value' - meaning the importance or worth of a trial. We determined face validity of the app using a set of ten published Phase 3 neuro-oncology clinical trials. A survey of neuro-oncology clinicians asked them to rank the same ten clinical trials, and to rank the seven metrics in terms of importance.

Results: The two highest app-ranked trials were in concordance with that of the survey respondents, and consistent with the two studies that have had the most impact on routine clinical practice in neuro-oncology. Of the seven metrics, surveyed clinicians considered patient outcomes and unmet need to be the most important when determining clinical trial value.

Conclusions: The metrics app was able to rank and produce a numerical 'value' for existing phase 3 neuro-oncology clinical trials. In the future, a related app to prospectively rank future trials at the startup stage could be developed to help centers determine which should be prioritized to be conducted at their site.

Keywords: Clinical trials; Decision analysis; Glioblastoma; Metrics; Multi-criteria decision analysis.

MeSH terms

  • Clinical Trials as Topic
  • Decision Support Techniques
  • Humans
  • Mobile Applications*
  • Neoplasms*
  • Reproducibility of Results