Matching the model with the evidence: comparing discrete event simulation and state-transition modeling for time-to-event predictions in a cost-effectiveness analysis of treatment in metastatic colorectal cancer patients

Koen Degeling; Mira D Franken; Anne M May; Martijn G H van Oijen; Miriam Koopman; Cornelis J A Punt; Maarten J IJzerman; Hendrik Koffijberg

doi:10.1016/j.canep.2018.09.008

Matching the model with the evidence: comparing discrete event simulation and state-transition modeling for time-to-event predictions in a cost-effectiveness analysis of treatment in metastatic colorectal cancer patients

Cancer Epidemiol. 2018 Dec:57:60-67. doi: 10.1016/j.canep.2018.09.008. Epub 2018 Oct 11.

Authors

Koen Degeling¹, Mira D Franken², Anne M May³, Martijn G H van Oijen⁴, Miriam Koopman², Cornelis J A Punt⁴, Maarten J IJzerman¹, Hendrik Koffijberg⁵

Affiliations

¹ Health Technology and Services Research Department, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands.
² Department of Medical Oncology, University Medical Centre, Utrecht University, Huispost B02.225, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
³ Julius Center for Health Sciences and Primary Care, University Medical Centre, Utrecht University, Huispost STR 6.131, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
⁴ Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, P.O. Box 22660, 1100 DD Amsterdam, The Netherlands.
⁵ Health Technology and Services Research Department, Technical Medical Centre, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Centre, Utrecht University, Huispost STR 6.131, P.O. Box 85500, 3508 GA Utrecht, The Netherlands. Electronic address: h.koffijberg@utwente.nl.

PMID: 30317148
DOI: 10.1016/j.canep.2018.09.008

Abstract

Background: Individual patient data, e.g. from clinical trials, often need to be extrapolated or combined with additional evidence when assessing long-term impact in cost-effectiveness modeling studies. Different modeling methods can be used to represent the complex dynamics of clinical practice; the choice of which may impact cost-effectiveness outcomes. We compare the use of a previously designed cohort discrete-time state-transition model (DT-STM) with a discrete event simulation (DES) model.

Methods: The original DT-STM was replicated and a DES model developed using AnyLogic software. Models were populated using individual patient data of a phase III study in metastatic colorectal cancer patients, and compared based on their evidence structure, internal validity, and cost-effectiveness outcomes. The DT-STM used time-dependent transition probabilities, whereas the DES model was populated using parametric distributions.

Results: The estimated time-dependent transition probabilities for the DT-STM were irregular and more sensitive to single events due to the required small cycle length and limited number of event observations, whereas parametric distributions resulted in smooth time-to-event curves for the DES model. Although the DT-STM and DES model both yielded similar time-to-event curves, the DES model represented the trial data more accurately in terms of mean health-state durations. The incremental cost-effectiveness ratio (ICER) was €172,443 and €168,383 per Quality Adjusted Life Year gained for the DT-STM and DES model, respectively.

Conclusion: DES represents time-to-event data from clinical trials more naturally and accurately than DT-STM when few events are observed per time cycle. As a consequence, DES is expected to yield a more accurate ICER.

Keywords: Cost-effectiveness; Discrete event simulation; Individual patient data; Markov modeling; State-transition modeling; Time-to-event.

Publication types

Comparative Study

MeSH terms

Colorectal Neoplasms / economics
Colorectal Neoplasms / therapy*
Cost-Benefit Analysis / methods*
Humans
Markov Chains*
Quality-Adjusted Life Years