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Department of Mathematical Sciences

Seminar Archives

On this page you can find information about seminars in this and previous academic years, where available on the database.

Statistics Seminars: Computer model uncertainty and health economic decision making

Presented by Mark Strong, Sheffield

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.

Contact i.r.vernon@durham.ac.uk for more information