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Comparing Strategies for Modeling Competing Risks in Discrete-Event Simulations: A Simulation Study and Illustration in Colorectal Cancer

Author

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  • Koen Degeling

    (Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands)

  • Hendrik Koffijberg

    (Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands)

  • Mira D. Franken

    (Department of Medical Oncology, University Medical Centre, Utrecht University, Utrecht, The Netherlands)

  • Miriam Koopman

    (Department of Medical Oncology, University Medical Centre, Utrecht University, Utrecht, The Netherlands)

  • Maarten J. IJzerman

    (Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, The Netherlands
    Cancer Health Services Research Unit, School of Population and Global Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
    Victorian Comprehensive Cancer Centre, Melbourne, Australia)

Abstract

Background. Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches. Methods. Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling approach was applied to uncensored individual patient data in a simulation study and a case study in colorectal cancer. Their performance was assessed in terms of relative event incidence difference, relative absolute event incidence difference, and relative entropy of time-to-event distributions. Differences in health economic outcomes were also illustrated for the case study. Results. In the simulation study, the ESPD and MDR approaches outperformed the ESD and UDR approaches, in terms of both event incidence differences and relative entropy. Disease pathway and data characteristics, such as the number of competing risks and overlap between competing time-to-event distributions, substantially affected the approaches’ performance. Although no considerable differences in health economic outcomes were observed, the case study showed that the ESPD approach was most sensitive to low event rates, which negatively affected performance. Conclusions. Based on overall performance, the recommended modeling approach for implementing competing risks in DES models is the MDR approach, which is defined according to the general strategy of selecting the time-to-event first and the corresponding event second. The ESPD approach is a less complex and equally performing alternative if sufficient observations are available for each competing event (i.e., the internal validity shows appropriate data representation).

Suggested Citation

  • Koen Degeling & Hendrik Koffijberg & Mira D. Franken & Miriam Koopman & Maarten J. IJzerman, 2019. "Comparing Strategies for Modeling Competing Risks in Discrete-Event Simulations: A Simulation Study and Illustration in Colorectal Cancer," Medical Decision Making, , vol. 39(1), pages 57-73, January.
    • Handle: RePEc:sae:medema:v:39:y:2019:i:1:p:57-73
    DOI: 10.1177/0272989X18814770
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    References listed on IDEAS

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    1. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
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    1. Jesús Isaac Vázquez-Serrano & Rodrigo E. Peimbert-García & Leopoldo Eduardo Cárdenas-Barrón, 2021. "Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review," IJERPH, MDPI, vol. 18(22), pages 1-20, November.
    2. David Glynn & John Giardina & Julia Hatamyar & Ankur Pandya & Marta Soares & Noemi Kreif, 2024. "Integrating decision modeling and machine learning to inform treatment stratification," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1772-1792, August.

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