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Decision analytic modeling in spinal surgery: a methodologic overview with review of current published literature
Steven J McAnany1, Muhammad A F Anwar1, Sheeraz A Qureshi1
1Department of Orthopaedic Surgery, Mount Sinai Medical Center, 5 E. 98th St, 9th Floor, New York, NY 10029, USA.
Background Context:
In recent years, there has been an increase in the number of decision analysis studies in the spine literature. Although there are several published reviews on the different types of decision analysis (cost-effectiveness, cost-benefit, cost-utility), there is limited information in the spine literature regarding the mathematical models used in these studies (decision tree, Markov modeling, Monte Carlo simulation).
Purpose:
The purpose of this review was to provide an overview of the types of decision analytic models used in spine surgery. A secondary aim was to provide a systematic overview of the most cited studies in the spine literature.
Study Design/Setting:
This is a systematic review of the available information from all sources regarding decision analytics and economic modeling in spine surgery.
Methods:
A systematic search of PubMed, Embase, and Cochrane review was performed to identify the most relevant peer-reviewed literature of decision analysis/cost-effectiveness analysis (CEA) models including decisions trees, Markov models, and Monte Carlo simulations. Additionally, CEA models based on investigational drug exemption studies were reviewed in particular detail, as these studies are prime candidates for economic modeling.
Results:
The initial review of the literature resulted in 712 abstracts. After two reviewer-assessment of abstract relevance and methodologic quality, 19 studies were selected: 12 with decision tree constructs and 7 with Markov models. Each study was assessed for methodologic quality and a review of the overall results of the model. A generalized overview of the mathematical construction and methodology of each type of model was also performed. Limitations, strengths, and potential applications to spine research were further explored.
Conclusions:
Decision analytic modeling represents a powerful tool both in the assessment of competing treatment options and potentially in the formulation of policy and reimbursement. Our review provides a generalized overview and a conceptual framework to help spine physicians with the construction of these models.
