Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer

Abstract

Objectives: The aim of this study is to compare the usefulness of two promising alternative modeling techniques, Timed Automata (TA) originating from informatics, and Discrete Event Simulation (DES) known in operations research, for modeling todays complex and personalized treatment decisions over time, involving multiple interactions and decision gates.

Methods: The usefulness of both modeling techniques was assessed in a case study on the treatment of metastatic Castration Resistant Prostate Cancer (mCRPC) in which Circulating Tumor Cells (CTC) may be used as a response marker for switching first to second line treatment. Techniques were compared on user-friendliness, input requirements, input possibilities, model checking facilities, and results. Input parameters were similar for both models, consisting of costs, QoL, treatment effectiveness, diagnostic performance, physicians’ behavior and survival. Primary outcome measures were health outcomes, expressed in QALYs, and costs.

Results: Modelling was considered easier using TA, as this approach allows independent modeling of the actors and elements comprising the treatment process, such as patients, physicians, tests and treatments, and their mutual interaction and communication. Furthermore, the statistical model checking feature in the TA software was found to be a powerful tool for validation. Input requirements and possibilities were similar for both modelling approaches in this case study. Both modelling approaches yield comparable results. Using TA, CTC reduced first and second line treatment by, on average, 108.9 and 107.6 days, respectively. Using DES, treatment was reduced by 83.6 and 85.0 days. CTC therefore reduced healthcare costs by €28,998 and €21,992 according to TA and DES, respectively.

Conclusions: Both Timed Automata and Discrete Event Simulation seem to be suitable for modeling complex and personalized treatment processes like that of mCRPC. Timed Automata is a new and interesting alternative modeling technique, as it allows explicit separation of model components and supports statistical model checking to validate models.
Original languageEnglish
Title of host publicationISPOR 18th Annual European Congress Research Abstracts
PublisherInternational Society for Pharmacoeconomics and Outcomes Research (ISPOR)
PagesA343-A344
Number of pages1
DOIs
StatePublished - Nov 2015

Publication series

NameValue in Health
PublisherInternational Society for Pharmacoeconomics and Outcomes Research (ISPOR)
Number7
Volume18
ISSN (Print)1098-3015
ISSN (Electronic)1524-4733

Fingerprint

Castration
Prostatic Neoplasms
Circulating Neoplastic Cells
Statistical Models
Operations Research
Physicians
Costs and Cost Analysis
Quality-Adjusted Life Years
Health Care Costs
Software
Communication
Outcome Assessment (Health Care)
Health

Keywords

  • IR-100078
  • METIS-316858
  • EWI-26886

Cite this

Degeling, K., Degeling, K., Koffijberg, H., Schivo, S., Langerak, R., & IJzerman, M. J. (2015). Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer. In ISPOR 18th Annual European Congress Research Abstracts (pp. A343-A344). (Value in Health; Vol. 18, No. 7). International Society for Pharmacoeconomics and Outcomes Research (ISPOR). DOI: 10.1016/j.jval.2015.09.159

Degeling, Koen; Degeling, K.; Koffijberg, Hendrik; Schivo, Stefano; Langerak, Romanus; IJzerman, Maarten Joost / Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer.

ISPOR 18th Annual European Congress Research Abstracts. International Society for Pharmacoeconomics and Outcomes Research (ISPOR), 2015. p. A343-A344 (Value in Health; Vol. 18, No. 7).

Research output: ScientificConference contribution

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abstract = "Objectives: The aim of this study is to compare the usefulness of two promising alternative modeling techniques, Timed Automata (TA) originating from informatics, and Discrete Event Simulation (DES) known in operations research, for modeling todays complex and personalized treatment decisions over time, involving multiple interactions and decision gates.Methods: The usefulness of both modeling techniques was assessed in a case study on the treatment of metastatic Castration Resistant Prostate Cancer (mCRPC) in which Circulating Tumor Cells (CTC) may be used as a response marker for switching first to second line treatment. Techniques were compared on user-friendliness, input requirements, input possibilities, model checking facilities, and results. Input parameters were similar for both models, consisting of costs, QoL, treatment effectiveness, diagnostic performance, physicians’ behavior and survival. Primary outcome measures were health outcomes, expressed in QALYs, and costs.Results: Modelling was considered easier using TA, as this approach allows independent modeling of the actors and elements comprising the treatment process, such as patients, physicians, tests and treatments, and their mutual interaction and communication. Furthermore, the statistical model checking feature in the TA software was found to be a powerful tool for validation. Input requirements and possibilities were similar for both modelling approaches in this case study. Both modelling approaches yield comparable results. Using TA, CTC reduced first and second line treatment by, on average, 108.9 and 107.6 days, respectively. Using DES, treatment was reduced by 83.6 and 85.0 days. CTC therefore reduced healthcare costs by €28,998 and €21,992 according to TA and DES, respectively.Conclusions: Both Timed Automata and Discrete Event Simulation seem to be suitable for modeling complex and personalized treatment processes like that of mCRPC. Timed Automata is a new and interesting alternative modeling technique, as it allows explicit separation of model components and supports statistical model checking to validate models.",
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Degeling, K, Degeling, K, Koffijberg, H, Schivo, S, Langerak, R & IJzerman, MJ 2015, Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer. in ISPOR 18th Annual European Congress Research Abstracts. Value in Health, no. 7, vol. 18, International Society for Pharmacoeconomics and Outcomes Research (ISPOR), pp. A343-A344. DOI: 10.1016/j.jval.2015.09.159

Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer. / Degeling, Koen; Degeling, K.; Koffijberg, Hendrik; Schivo, Stefano; Langerak, Romanus; IJzerman, Maarten Joost.

ISPOR 18th Annual European Congress Research Abstracts. International Society for Pharmacoeconomics and Outcomes Research (ISPOR), 2015. p. A343-A344 (Value in Health; Vol. 18, No. 7).

Research output: ScientificConference contribution

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N2 - Objectives: The aim of this study is to compare the usefulness of two promising alternative modeling techniques, Timed Automata (TA) originating from informatics, and Discrete Event Simulation (DES) known in operations research, for modeling todays complex and personalized treatment decisions over time, involving multiple interactions and decision gates.Methods: The usefulness of both modeling techniques was assessed in a case study on the treatment of metastatic Castration Resistant Prostate Cancer (mCRPC) in which Circulating Tumor Cells (CTC) may be used as a response marker for switching first to second line treatment. Techniques were compared on user-friendliness, input requirements, input possibilities, model checking facilities, and results. Input parameters were similar for both models, consisting of costs, QoL, treatment effectiveness, diagnostic performance, physicians’ behavior and survival. Primary outcome measures were health outcomes, expressed in QALYs, and costs.Results: Modelling was considered easier using TA, as this approach allows independent modeling of the actors and elements comprising the treatment process, such as patients, physicians, tests and treatments, and their mutual interaction and communication. Furthermore, the statistical model checking feature in the TA software was found to be a powerful tool for validation. Input requirements and possibilities were similar for both modelling approaches in this case study. Both modelling approaches yield comparable results. Using TA, CTC reduced first and second line treatment by, on average, 108.9 and 107.6 days, respectively. Using DES, treatment was reduced by 83.6 and 85.0 days. CTC therefore reduced healthcare costs by €28,998 and €21,992 according to TA and DES, respectively.Conclusions: Both Timed Automata and Discrete Event Simulation seem to be suitable for modeling complex and personalized treatment processes like that of mCRPC. Timed Automata is a new and interesting alternative modeling technique, as it allows explicit separation of model components and supports statistical model checking to validate models.

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Degeling K, Degeling K, Koffijberg H, Schivo S, Langerak R, IJzerman MJ. Comparison of Timed Automata with Discrete Event Simulation for Modeling Personalized Treatment Decisions: the Case of Metastatic Castration Resistant Prostate Cancer. In ISPOR 18th Annual European Congress Research Abstracts. International Society for Pharmacoeconomics and Outcomes Research (ISPOR). 2015. p. A343-A344. (Value in Health; 7). Available from, DOI: 10.1016/j.jval.2015.09.159