A health economic model to assess the cost-effectiveness of OPTIFAST for the treatment of obesity in the United States

Abstract INTRODUCTION Surgery is an effective treatment for many pharmacoresistant temporal lobe epilepsy patients, but incurs considerable cost. It is unknown whether surgery and surgical evaluation are cost-effective strategies in the United States. We aim to evaluate whether 1) surgery is cost-effective for patients who have been deemed surgical candidates when compared to continued medical management, 2) surgical evaluation is cost-effective for patients who have drug-resistant temporal epilepsy and may or may not ultimately be deemed surgical candidates METHODS We use a Monte Carlo simulation method to assess the cost-effectiveness of surgery and surgical evaluation over a lifetime horizon. Patients transition between two health states (‘seizure free’ and ‘having seizures’) as part of a Markov process, based on literature estimates. We adopt both healthcare and societal perspectives, including direct healthcare costs and indirect costs such as lost earnings by patients and care providers. We estimate variability of model predictions using probabilistic and deterministic sensitivity analyses. RESULTS 1) Epilepsy surgery is cost effective in surgically eligible patients by virtue of being cost saving and more effective than medical management in the long run, with 95% of 10 000 Monte Carlo simulations favoring surgery. From a societal perspective, surgery becomes cost effective within 3 yr. At 5 yr, surgery has an incremental cost-effectiveness ratio (ICER) of $31,600, which is significantly below the societal willingness-to-pay (∼ $100,000/quality-adjusted life years (QALY)) and comparable to hip/knee arthroplasty. 2) Surgical evaluation is cost-effective in pharmacoresistant patients even if the probability of being deemed a surgical candidate is low (5%-10%). Even if the probability of surgical eligibility is only 10%, surgical referral has an ICER of $96,000/QALY, which is below societal willingness-to-pay. CONCLUSION Epilepsy surgery and surgical evaluation are both cost-effective strategies in the United States. Pharmacoresistant temporal lobe epilepsy patients should be referred for surgical evaluation without hesitation on cost-effectiveness grounds.

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Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression

Medical Decision Making ◽

10.1177/0272989x19837983 ◽

2019 ◽

Vol 39 (4) ◽

pp. 347-359 ◽

Cited By ~ 2

Author(s):

Anna Heath ◽

Ioanna Manolopoulou ◽

Gianluca Baio

Keyword(s):

Health Economic ◽

Nonlinear Regression ◽

Economic Model ◽

Trial Design ◽

Expected Value ◽

Moment Matching ◽

Sample Sizes ◽

Sample Information ◽

Health Economic Model ◽

The Cost

Background. The expected value of sample information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty about the parameters underlying a health economic model. This has potential as a tool for trial design; the cost and value of different designs could be compared to find the trial with the greatest net benefit. However, despite recent developments, EVSI analysis can be slow, especially when optimizing over a large number of different designs. Methods. This article develops a method to reduce the computation time required to calculate the EVSI across different sample sizes. Our method extends the moment-matching approach to EVSI estimation to optimize over different sample sizes for the underlying trial while retaining a similar computational cost to a single EVSI estimate. This extension calculates the posterior variance of the net monetary benefit across alternative sample sizes and then uses Bayesian nonlinear regression to estimate the EVSI across these sample sizes. Results. A health economic model developed to assess the cost-effectiveness of interventions for chronic pain demonstrates that this EVSI calculation method is fast and accurate for realistic models. This example also highlights how different trial designs can be compared using the EVSI. Conclusion. The proposed estimation method is fast and accurate when calculating the EVSI across different sample sizes. This will allow researchers to realize the potential of using the EVSI to determine an economically optimal trial design for reducing uncertainty in health economic models. Limitations. Our method involves rerunning the health economic model, which can be more computationally expensive than some recent alternatives, especially in complex models.

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