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• W3008662175 abstract "To the Editor: We read with great interest the article by Stein.1 We would like to congratulate Dr Stein for doing a very commendable job in bringing to light a pertinent area for improvement in neurosurgical academia, that is, the paucity of cost-effectiveness analysis (CEA) studies for neurosurgical interventions. In this paper, he highlighted the importance and relevance of CEA studies in shaping the health-care landscape, and also concisely and clearly explained the concepts behind CEA. Dr Stein1 has made a timely call for methodologically-sound CEA studies in neurosurgery. CEA is especially relevant in neurosurgery due to neurosurgical interventions being one of the most expensive in medicine.2 The annual cost of spine service in the United States has been estimated to be over 100 billion USD, a number that has contributed to a 175% increase in global health-care costs over the past decade in the United States.3-6 Similarly, a recent analysis by the Barrow Neurological Institute showed that patient out-of-pocket spending associated with cranial neurosurgical procedures has increased significantly from 2013 to 2016 even after controlling for inflation, case-mix differences, and partial fiscal periods.7 Yet, despite these growing costs of neurosurgical interventions, there remains a paucity of CEA studies within neurosurgical literature. Furthermore, there is a discrepancy in reported CEA studies across the neurosurgical subspecialties, with spine being the only field with a significant number of CEA studies published.2 We find no consolation in the fact that a majority of self-proclaimed CEA studies in neurosurgery do not follow stringent CEA methodology.2 Even in the field of spine, which is currently the “leader” of CEA studies in neurosurgery, many purported cost-analysis studies are in fact cost descriptions that do not involve proper CEA analysis.8-11 As such, it is timely that Dr Stein1 has enlightened the reader on the concepts behind CEA and the limitations that need to be considered in cost-analysis studies. On this note, we would like to further build on Dr Stein's explanation of CEA with a brief discussion of one of the most commonly used models in CEA – the Markov model. The Markov model defines a disease in distinct, mutually-exclusive states. These states represent clinically and economically significant events in the disease process, for example asymptomatic, symptomatic, or dead. At every stage of the analysis, participants have to be in one of the states, and can only be in one state at any one time. Each state is associated with its own set of effects and costs. After every cycle of a pre-determined number of years, participants can transit across states, for example, from asymptomatic to symptomatic or from symptomatic to dead. Transition probabilities are attached to each transition between states. At the end of all the cycles, the total costs and effects are calculated to find the incremental cost-effectiveness ratio (ICER). This can be done using a simple matrix algebra which would demonstrate the time spent in each state and the overall expected value of each outcome. The Markov model serves as a reliable tool for calculating the ICER for each treatment arm in the decision tree illustrated by Dr Stein in Figure 1.1 As with all analyses, the Markov model comes with certain inherent limitations, with the most important being the Markovian assumption. This assumption states that the probability of transitioning out of a state is independent of the states that a patient had been in prior to entering the current state. This limitation exists because the Markovian model assumes constant transition probabilities over time. Fortunately, the experienced modeler would be able to overcome this limitation by adopting time-dependent transition probabilities and using distinct disease states to simulate specific patient histories. Interested readers are encouraged to read the article by Briggs and Sculpher12 as well as that by Sonnenberg and Beck13 for a more detailed explanation of the Markov model. We hope that this letter would complement Dr Stein's article1 to spur a greater number of CEA studies in the neurosurgical literature to come. Disclosures The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article." @default.
• W3008662175 created "2020-03-06" @default.
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• W3008662175 date "2020-02-20" @default.
• W3008662175 modified "2023-10-17" @default.
• W3008662175 title "Letter: Cost-Effectiveness Research in Neurosurgery: We Can and We Must" @default.
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• W3008662175 doi "https://doi.org/10.1093/neuros/nyaa044" @default.
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