Statistics Seminars: Computer model uncertainty and health economic decision making
11 November 2013 14:00 in CM221
Health economic models predict the costs and health effects associated with competing decision options (e.g. recommend drug X versus Y). Such models are typically deterministic and `law-driven', rather than fitted to data. Current practice is to quantify input uncertainty, but to ignore uncertainty due to deficiencies in model structure.
However, ignoring `structural' uncertainty makes it difficult to answer the question: given a relatively simple but imperfect model, is there value in incorporating additional complexity to better describe the decision problem, or is the simple model `good enough'?
To address this problem we propose a model discrepancy based approach. Firstly, the model is decomposed into a series of sub-functions. The decomposition is chosen such that the output of each sub-function is a real world observable quantity. Next, where it is judged that a sub-function would not necessarily result in the `true' value of the corresponding real world quantity, even if its inputs were `correct', a discrepancy term is introduced. Beliefs about the discrepancies are specified via a joint distribution over discrepancies and model inputs.
To answer the question `is the model good enough' we then compute the expected value of perfect information (EVPI) for the discrepancy terms, interpreting this as an upper bound on the `expected value of model improvement' (EVMI). If the expected value of model improvement is small then we have some reassurance that the model is good enough for the decision.
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