Modelling the lifetime cost-effectiveness of radical prostatectomy, radiotherapy and active monitoring for men with clin
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RESEARCH ARTICLE
Open Access
Modelling the lifetime cost-effectiveness of radical prostatectomy, radiotherapy and active monitoring for men with clinically localised prostate cancer from median 10year outcomes in the ProtecT randomised trial S. Sanghera1* , S. Mohiuddin1,2, J. Coast1, K. Garfield1,3, S. Noble1, C. Metcalfe3, J. A. Lane3,4, E. L. Turner4, D. Neal5, F. C. Hamdy5†, R. M. Martin4,6†, J. L. Donovan2,4† and for the ProtecT study group
Abstract Background: Optimal management strategies for clinically localised prostate cancer are debated. Using median 10year data from the largest randomised controlled trial to date (ProtecT), the lifetime cost-effectiveness of three major treatments (radical radiotherapy, radical prostatectomy and active monitoring) was explored according to age and risk subgroups. Methods: A decision-analytic (Markov) model was developed and informed by clinical input. The economic evaluation adopted a UK NHS perspective and the outcome was cost per Quality-Adjusted Life Year (QALY) gained (reported in UK£), estimated using EQ-5D-3L. Results: Costs and QALYs extrapolated over the lifetime were mostly similar between the three randomised strategies and their subgroups, but with some important differences. Across all analyses, active monitoring was associated with higher costs, probably associated with higher rates of metastatic disease and changes to radical treatments. When comparing the value of the strategies (QALY gains and costs) in monetary terms, for both low-risk prostate cancer subgroups, radiotherapy generated the greatest net monetary benefit (£293,446 [95% CI £282,811 to £299, 451] by D’Amico and £292,736 [95% CI £284,074 to £297,719] by Grade group 1). However, the sensitivity analysis highlighted uncertainty in the finding when stratified by Grade group, as radiotherapy had 53% probability of costeffectiveness and prostatectomy had 43%. In intermediate/high risk groups, using D’Amico and Grade group > = 2, (Continued on next page)
* Correspondence: [email protected] † F. C. Hamdy, R. M. Martin and J. L. Donovan contributed equally to this work. 1 Health Economics Bristol (HEB), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1NU, UK Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need
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