Edward C F Wilson1, Juliet A Usher-Smith2, Jon Emery3, Pippa G Corrie4, Fiona M Walter3. 1. Cambridge Centre for Health Services Research, Institute of Public Health, University of Cambridge, Cambridge, UK. Electronic address: ed.wilson@medschl.cam.ac.uk. 2. Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. 3. Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Department of General Practice, Centre for Cancer Research, Faculty of Medicine, Dentistry and Health Science, Victorian Comprehensive Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia. 4. Cambridge Cancer Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Abstract
BACKGROUND: Expert elicitation is required to inform decision making when relevant "better quality" data either do not exist or cannot be collected. An example of this is to inform decisions as to whether to screen for melanoma. A key input is the counterfactual, in this case the natural history of melanoma in patients who are undiagnosed and hence untreated. OBJECTIVES: To elicit expert opinion on the probability of disease progression in patients with melanoma that is undetected and hence untreated. METHODS: A bespoke webinar-based expert elicitation protocol was administered to 14 participants in the United Kingdom, Australia, and New Zealand, comprising 12 multinomial questions on the probability of progression from one disease stage to another in the absence of treatment. A modified Connor-Mosimann distribution was fitted to individual responses to each question. Individual responses were pooled using a Monte-Carlo simulation approach. Participants were asked to provide feedback on the process. RESULTS: A pooled modified Connor-Mosimann distribution was successfully derived from participants' responses. Feedback from participants was generally positive, with 86% willing to take part in such an exercise again. Nevertheless, only 57% of participants felt that this was a valid approach to determine the risk of disease progression. Qualitative feedback reflected some understanding of the need to rely on expert elicitation in the absence of "hard" data. CONCLUSIONS: We successfully elicited and pooled the beliefs of experts in melanoma regarding the probability of disease progression in a format suitable for inclusion in a decision-analytic model.
BACKGROUND: Expert elicitation is required to inform decision making when relevant "better quality" data either do not exist or cannot be collected. An example of this is to inform decisions as to whether to screen for melanoma. A key input is the counterfactual, in this case the natural history of melanoma in patients who are undiagnosed and hence untreated. OBJECTIVES: To elicit expert opinion on the probability of disease progression in patients with melanoma that is undetected and hence untreated. METHODS: A bespoke webinar-based expert elicitation protocol was administered to 14 participants in the United Kingdom, Australia, and New Zealand, comprising 12 multinomial questions on the probability of progression from one disease stage to another in the absence of treatment. A modified Connor-Mosimann distribution was fitted to individual responses to each question. Individual responses were pooled using a Monte-Carlo simulation approach. Participants were asked to provide feedback on the process. RESULTS: A pooled modified Connor-Mosimann distribution was successfully derived from participants' responses. Feedback from participants was generally positive, with 86% willing to take part in such an exercise again. Nevertheless, only 57% of participants felt that this was a valid approach to determine the risk of disease progression. Qualitative feedback reflected some understanding of the need to rely on expert elicitation in the absence of "hard" data. CONCLUSIONS: We successfully elicited and pooled the beliefs of experts in melanoma regarding the probability of disease progression in a format suitable for inclusion in a decision-analytic model.
Authors: Laura Bojke; Marta Soares; Karl Claxton; Abigail Colson; Aimée Fox; Christopher Jackson; Dina Jankovic; Alec Morton; Linda Sharples; Andrea Taylor Journal: Health Technol Assess Date: 2021-06 Impact factor: 4.014
Authors: Andrew Bryant; Michael Grayling; Shaun Hiu; Ketankumar Gajjar; Eugenie Johnson; Ahmed Elattar; Luke Vale; Dawn Craig; Raj Naik Journal: BMJ Open Date: 2022-08-29 Impact factor: 3.006