Abstract
A medical decision model is a representation of a healthcare decision process with observable outcomes enabling healthcare decision makers to choose among competing courses of action. Its credibility and value depend largely on three components: the plausibility of the structure as measured against the problem concept, the quality of the data that feed the model parameters, and the validity of the outcome structure. Health decision models in use today include decision trees, state-transition models (STM), and dynamic transmission models. Formal medical decision models are now almost exclusively represented as STMs which assume that an individual is always in one of a finite number of conditions (States) and events of interest to the problem are characterized as movements from one state to another (transition). Two common STMs are the Markov cohort, where a simulated group of patients begins in a particular health state and transitions within each time unit which are accomplished according to probabilities and the microsimulation, where a cohort of patients moves from state to state one at a time, using a random number based on probabilities to effect the state transitions. In this chapter best practices for modeling medical decisions will be highlighted, including the use of a worked example.
The paper was supported by Award Number P30CA006927 from the National Cancer Institute. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
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Notes
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This shows one of the challenges in model building—a simplified Markov model has transitions occurring with the first cycle. We could build a DES that delays this transition for a specified period of time, or we could make a more complex Markov model.
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Beck, J.R. (2016). Modeling Medical Decisions. In: Diefenbach, M., Miller-Halegoua, S., Bowen, D. (eds) Handbook of Health Decision Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3486-7_3
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