Simulation and prediction modelling
Decision modelling in economic evaluation of health care interventions involves the use of mathematical relationships to describe possible consequences arising from a set of alternative options under the evaluation. Decision models provide an analytic framework to synthesize evidence and address the decision problem. Modelling is used to overcome the limitations of economic evaluation alongside a randomized clinical trial, for example, to extrapolate clinical trial results beyond the follow-up period, to link intermediate to final outcome and to generalize results to other patient groups or health care settings. For chronic diseases such as diabetes and rheumatoid arthritis, models that predict disease progression and occurrence of complications and comorbid conditions are essential for optimizing healthcare policies as it can take many years to observe the impact of a new therapy on health outcomes. Simulation modelling is now increasingly used in many clinical contexts, such as Genomic Medicine, to capture patient and clinical heterogeneity and its consequences on costs and outcomes.
The Health Economics Unit has been leading the development of many decision analytic models, ranging from cohort models for cost-effectiveness of genetic testing to patient-level simulation models. Our team has also led the development of predictive models and tools for cardiovascular risk and life expectancy of patients with diabetes.
Project highlights:
Development and validation of a health policy simulation model for cardiovascular disease
Development and validation of a health policy simulation model for Type 1 diabetes
Prediction tools
An Android app for cardiovascular risk assessment in remote Indigenous Australia
An iOS app for cardiovascular risk assessment in remote Indigenous Australia
Calibrated Framingham Risk Calculator for Indigenous Australians
Selected publications
Tran-Duy A, Morrisroe K, Clarke PM, et al. Cost-effectiveness of combination therapy for patients with systemic sclerosis-related pulmonary arterial hypertension. Journal of the American Heart Association 2021; DOI: 10.1161/JAHA.119.015816. In this study a probabilistic discrete-time simulation model was developed to assess the cost-effectiveness of combination therapy compared to monotherapy for treatment of systemic sclerosis-related pulmonary arterial hypertension.
Goranitis I, Wu Y, Lunke S, et al. Is faster better? An economic evaluation of rapid and ultra-rapid genomic testing in critically ill infants and children. Genet Med. 2022;24(5):1037-1044. doi:10.1016/j.gim.2022.01.013
Wu Y, Balasubramaniam S, Rius R, Thorburn DR, Christodoulou J, Goranitis I. Genomic sequencing for the diagnosis of childhood mitochondrial disorders: a health economic evaluation. Eur J Hum Genet. 2021 Jun 8. doi: 10.1038/s41431-021-00916-8. Epub ahead of print.
Tran-Duy A, Knight J, Palmer A, …, Clarke PM. A patient-level model to estimate lifetime health outcomes of patients with type 1 diabetes. Diabetes Care 2020; 43:1741-1749. This article describes the development of a comprehensive patient-level simulation model that can be used to predict the occurrence of complications and progression of risk factors in patients with type 1 diabetes, and to conduct economic evaluations of new interventions for treatment of type 1 diabetes.
Tran-Duy A, Mcdermott R, Knight J, Hua X, Barr EL, Arabena K, Palmer A and Clarke PM. Development and use of prediction models for classification of cardiovascular risk of remote indigenous Australians. Heart, Lung and Circulation 2020; 29:374-383. This is the first study that developed a prediction model for cardiovascular risk in Indigenous Australians.
Hua X., McDermott R., Lung T., Wenitong M., Tran-Duy A., Li M., & CLARKE, P. Validation and recalibration of the Framingham cardiovascular disease risk models in an Australian Indigenous cohort. Eur J Prev Cardiol 2017, 24, 1660-1669. In this study the Framingham equations for cardiovascular risk prediction were calibrated to be used in remote Indigenous Australians.
Schilling C., Dalziel K., Nunn R., Du Plessis K., Shi W.Y., Celermajer D., ... & Bullock A. The Fontan epidemic: Population projections from the Australia and New Zealand Fontan Registry. International Journal of Cardiology 2016;219:14-19.
Hayes AJ., Leal J., Gray AM., Holman RR., & Clarke PM. UKPDS Outcomes Model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia 2013; 1-9.