Koen Degeling1, Stefano Schivo2, Niven Mehra3, Hendrik Koffijberg4, Rom Langerak2, Johann S de Bono5, Maarten J IJzerman4. 1. Health Technology and Services Research Department, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands. Electronic address: k.degeling@utwente.nl. 2. Formal Methods and Tools Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, The Netherlands. 3. Clinical Studies Department, The Institute of Cancer Research, London, UK. 4. Health Technology and Services Research Department, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands. 5. Prostate Cancer Unit, The Institute of Cancer Research, London, UK.
Abstract
BACKGROUND: With the advent of personalized medicine, the field of health economic modeling is being challenged and the use of patient-level dynamic modeling techniques might be required. OBJECTIVES: To illustrate the usability of two such techniques, timed automata (TA) and discrete event simulation (DES), for modeling personalized treatment decisions. METHODS: An early health technology assessment on the use of circulating tumor cells, compared with prostate-specific antigen and bone scintigraphy, to inform treatment decisions in metastatic castration-resistant prostate cancer was performed. Both modeling techniques were assessed quantitatively, in terms of intermediate outcomes (e.g., overtreatment) and health economic outcomes (e.g., net monetary benefit). Qualitatively, among others, model structure, agent interactions, data management (i.e., importing and exporting data), and model transparency were assessed. RESULTS: Both models yielded realistic and similar intermediate and health economic outcomes. Overtreatment was reduced by 6.99 and 7.02 weeks by applying circulating tumor cell as a response marker at a net monetary benefit of -€1033 and -€1104 for the TA model and the DES model, respectively. Software-specific differences were observed regarding data management features and the support for statistical distributions, which were considered better for the DES software. Regarding method-specific differences, interactions were modeled more straightforward using TA, benefiting from its compositional model structure. CONCLUSIONS: Both techniques prove suitable for modeling personalized treatment decisions, although DES would be preferred given the current software-specific limitations of TA. When these limitations are resolved, TA would be an interesting modeling alternative if interactions are key or its compositional structure is useful to manage multi-agent complex problems.
BACKGROUND: With the advent of personalized medicine, the field of health economic modeling is being challenged and the use of patient-level dynamic modeling techniques might be required. OBJECTIVES: To illustrate the usability of two such techniques, timed automata (TA) and discrete event simulation (DES), for modeling personalized treatment decisions. METHODS: An early health technology assessment on the use of circulating tumor cells, compared with prostate-specific antigen and bone scintigraphy, to inform treatment decisions in metastatic castration-resistant prostate cancer was performed. Both modeling techniques were assessed quantitatively, in terms of intermediate outcomes (e.g., overtreatment) and health economic outcomes (e.g., net monetary benefit). Qualitatively, among others, model structure, agent interactions, data management (i.e., importing and exporting data), and model transparency were assessed. RESULTS: Both models yielded realistic and similar intermediate and health economic outcomes. Overtreatment was reduced by 6.99 and 7.02 weeks by applying circulating tumor cell as a response marker at a net monetary benefit of -€1033 and -€1104 for the TA model and the DES model, respectively. Software-specific differences were observed regarding data management features and the support for statistical distributions, which were considered better for the DES software. Regarding method-specific differences, interactions were modeled more straightforward using TA, benefiting from its compositional model structure. CONCLUSIONS: Both techniques prove suitable for modeling personalized treatment decisions, although DES would be preferred given the current software-specific limitations of TA. When these limitations are resolved, TA would be an interesting modeling alternative if interactions are key or its compositional structure is useful to manage multi-agent complex problems.
Authors: Deborah A Marshall; Luiza R Grazziotin; Dean A Regier; Sarah Wordsworth; James Buchanan; Kathryn Phillips; Maarten Ijzerman Journal: Value Health Date: 2020-03-26 Impact factor: 5.725
Authors: Koen Degeling; Amanda Pereira-Salgado; Niall M Corcoran; Paul C Boutros; Peter Kuhn; Maarten J IJzerman Journal: Eur Urol Open Sci Date: 2021-03-26