Development and Validation of txSim: A Model of Advanced Lung Cancer Treatment in Australia

Preston Ngo (), Deme Karikios, David Goldsbury, Stephen Wade, Zarnie Lwin, Brett G. M. Hughes, Kwun M. Fong, Karen Canfell and Marianne Weber
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Preston Ngo: The University of Sydney, A Joint Venture with Cancer Council NSW
Deme Karikios: Nepean Hospital
David Goldsbury: The University of Sydney, A Joint Venture with Cancer Council NSW
Stephen Wade: The University of Sydney, A Joint Venture with Cancer Council NSW
Zarnie Lwin: Royal Brisbane and Women’s Hospital
Brett G. M. Hughes: Royal Brisbane and Women’s Hospital
Kwun M. Fong: The Prince Charles Hospital
Karen Canfell: The University of Sydney, A Joint Venture with Cancer Council NSW
Marianne Weber: The University of Sydney, A Joint Venture with Cancer Council NSW

PharmacoEconomics, 2023, vol. 41, issue 11, No 7, 1525-1537

Abstract: Abstract Background and Objective Since 2016, new therapies have transformed the standard of care for lung cancer, creating a need for up-to-date evidence for health economic modelling. We developed a discrete event simulation of advanced lung cancer treatment to provide estimates of survival outcomes and healthcare costs in the Australian setting that can be updated as new therapies are introduced. Methods Treatment for advanced lung cancer was modelled under a clinician-specified treatment algorithm for Australia in 2022. Prevalence of lung cancer subpopulations was extracted from cBioPortal and the Sax Institute’s 45 and Up Study, a large prospective cohort linked to cancer registrations. All costs were from the health system perspective for the year 2020. Pharmaceutical and molecular diagnostic costs were obtained from public reimbursement fees, while other healthcare costs were obtained from health system costs in the 45 and Up Study. Treatment efficacy was obtained from clinical trials and observational study data. Costs and survival were modelled over a 10-year horizon. Uncertainty intervals were generated with probabilistic sensitivity analyses. Overall survival predictions were validated against real-world studies. Results Under the 2022 treatment algorithm, estimated mean survival and costs for advanced lung cancer 10 years post-diagnosis were 16.4 months (95% uncertainty interval [UI]: 14.7–18.1) and AU$116,069 (95% UI: $107,378–$124,933). Survival and costs were higher assuming optimal treatment utilisation rates (20.5 months, 95% UI: 19.1–22.5; $154,299, 95% UI: $146,499–$161,591). The model performed well in validation, with good agreement between predicted and observed survival in real-world studies. Conclusions Survival improvements for advanced lung cancer have been accompanied by growing treatment costs. The estimates reported here can be used for budget planning and economic evaluations of interventions across the spectrum of cancer control.

Date: 2023
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DOI: 10.1007/s40273-023-01291-6

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