Cost-Effectiveness Analyses in Orthopaedic Surgery: Raising the Bar

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Rising health-care spending has renewed emphasis on value-based care for institutions and policymakers. As a result, economic evidence increasingly is being considered in the shaping of clinical guidelines and practices1. Cost-effectiveness analysis using decision analytic models is a widely used methodology to establish the value of specific clinical interventions2. Models are informed by data from multiple sources, and are therefore capable of projecting outcomes and costs over longer time frames than the durations of randomized controlled trials (RCTs). Additionally, model-based evaluations can compare multiple interventions in the same analysis.
In 1996, The Journal of the American Medical Association published the recommendations of the First Panel on Cost-Effectiveness in Health and Medicine, which summarized the state of the growing field of cost-effectiveness analysis and provided guidance on the design and conduct of cost-effectiveness analyses3. Over the more than 20 years since those recommendations, there has been a drastic increase in the number of cost-effectiveness studies published in all areas of medicine, including the field of orthopaedic surgery, albeit with variable methodologic rigor4,5. In September 2016, the Second Panel on Cost-Effectiveness in Health and Medicine (the “Second Panel”) updated these recommendations to establish standard analytic and reporting practices, taking into consideration methodologic advancements over the last 2 decades6. The Second Panel’s recommendations focus particularly on standards of model development and reporting, as well as addressing data uncertainty.
In applying the Second Panel’s recommendations, it is important to note relevant differences between interventions in orthopaedic surgery compared with other areas in medicine. Surgical procedures often occur as discrete episodes of care, associated with high up-front costs, a high likelihood of symptom relief, and low-to-moderate rates of complications. But symptom relief as a result of surgery may have time limits, and the sustainability of such relief may require additional surgeries over time. A good example of such a trajectory is total hip arthroplasty. This is typically a one-time procedure associated with long-term pain relief and functional benefit, a low rate of complications, and some risk of additional surgery, which may increase over time. In such scenarios, lifetime modeling, using knowledge or assumptions about the persistence of benefit over time and symptomatic trajectory among those not undergoing surgery, is critical.
Given the substantial variability in the quality of manuscripts reporting cost-effectiveness analyses in the field of orthopaedic surgery, we sought to help “raise the bar” of such studies by providing a focused reporting checklist for cost-effectiveness analyses in orthopaedic surgery that is consistent with the Second Panel’s recommendations (Table I). We emphasize that the modeling approach should fit the critical details of the clinical or policy question that is being examined. The model’s structure should be consistent with the nature of the clinical problem, and should capture all of the benefits and the adverse events of the treatments under consideration over both the short and long time horizons. A Markov state-transition cohort analysis or Monte Carlo simulation should be used to simulate patient cohorts over time, where the risk of complications or revision varies over time, while decision tree models should be reserved for more simplistic scenarios. The time horizon utilized must be long enough to evaluate all relevant costs and benefits to the patient over his or her lifetime. Model-based evaluations should include the means of internal and external model validation.
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