Abstract
Applied cost-effectiveness analysis models are an important tool for assessing health and economic effects of healthcare interventions but are not best suited for illustrating methods. Our objective is to provide a simple, open-source model for the simulation of disease-screening cost-effectiveness for teaching and research purposes. We introduce our model and provide an initial application to examine changes to the efficiency frontier as input parameters vary and to demonstrate face validity. We described a vectorised, discrete-event simulation of screening in R with an Excel interface to define parameters and inspect principal results. An R Shiny app permits dynamic interpretation of simulation outputs. An example with 8161 screening strategies illustrates the cost and effectiveness of varying the disease sojourn time, treatment effectiveness, and test performance characteristics and costs on screening policies. Many of our findings are intuitive and straightforward, such as a reduction in screening costs leading to decreased overall costs and improved cost-effectiveness. Others are less obvious and depend on whether we consider gross outcomes or those net to no screening. For instance, enhanced treatment of symptomatic disease increases gross effectiveness, but reduces the net effectiveness and cost-effectiveness of screening. A lengthening of the preclinical sojourn time has ambiguous effects relative to no screening, as cost-effectiveness improves for some strategies but deteriorates for others. Our simple model offers an accessible platform for methods research and teaching. We hope it will serve as a public good and promote an intuitive understanding of the cost-effectiveness of screening.
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Details
; O’Mahony, James F 1
; van Rosmalen, Joost 2
1 Trinity College Dublin, Centre for Health Policy and Management, Dublin, Ireland (GRID:grid.8217.c) (ISNI:0000 0004 1936 9705)
2 Erasmus Medical Centre, Department of Biostatistics, Rotterdam, The Netherlands (GRID:grid.5645.2) (ISNI:000000040459992X); Erasmus Medical Centre, Department of Epidemiology, Rotterdam, The Netherlands (GRID:grid.5645.2) (ISNI:000000040459992X)





