Abstract
BackgroundEconomic-evaluations using decision analytic models such as Markov-models (MM), and discrete-event-simulations (DES) are high value adds in allocating resources. The choice of modelling method is critical because an inappropriate model yields results that could lead to flawed decision making. The aim of this study was to compare cost-effectiveness when MM and DES were used to model results of transplanting a lower-quality kidney versus remaining waitlisted for a kidney.MethodsCost-effectiveness was assessed using MM and DES. We used parametric survival models to estimate the time-dependent transition probabilities of MM and distribution of time-to-event in DES. MMs were simulated in 12 and 6 monthly cycles, out to five and 20-year time horizon.ResultsDES model output had a close fit to the actual data. Irrespective of the modelling method, the cycle length of MM or the time horizon, transplanting a low-quality kidney as compared to remaining waitlisted was the dominant strategy. However, there were discrepancies in costs, effectiveness and net monetary benefit (NMB) among different modelling methods. The incremental NMB of the MM in the 6-months cycle lengths was a closer fit to the incremental NMB of the DES. The gap in the fit of the two cycle lengths to DES output reduced as the time horizon increased.ConclusionDifferent modelling methods were unlikely to influence the decision to accept a lower quality kidney transplant or remain waitlisted on dialysis. Both models produced similar results when time-dependant transition probabilities are used, most notable with shorter cycle lengths and longer time-horizons.
Highlights
Decision makers in health services commonly make resource allocation choices, aiming to select for high value care
Lower quality donor kidneys are associated with increased risks of graft failure, earlier return to dialysis and higher post-transplantation costs [3, 4]
The ever-increasing demand for donor kidneys is driving decision makers to consider lower quality kidneys, predicated on the premise that any transplant offers superior patient survival and quality of life compared to remaining on dialysis
Summary
Decision makers in health services commonly make resource allocation choices, aiming to select for high value care. The processes of choice selection include arbitrary, Senanayake et al Health Economics Review (2021) 11:13 specific, with deployment of inappropriate models yielding results that could be flawed/lower value decision making [2]. The ever-increasing demand for donor kidneys is driving decision makers to consider lower quality kidneys, predicated on the premise that any transplant offers superior patient survival and quality of life compared to remaining on dialysis. Decision analytic models bring rigour to the premise, systematically assessing the relative cost/benefits of transplanting even a lower quality kidney compared with remaining on dialysis. Axelrod et al was the only publication we found deploying DES modelling [6] They reported transplanting a lower quality kidney has an incremental cost-effectiveness ratio (ICER) of USD 32,870 per quality adjusted life years (QALY) compared with remaining on dialysis. The aim of this study was to compare cost-effectiveness when MM and DES were used to model results of transplanting a lower-quality kidney versus remaining waitlisted for a kidney
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