NDLI: Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial

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Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial

Content Provider	PubMed Central
Author	Jackson, Christopher Mark, Jit Sharples, Linda Deangelis, Daniela
Abstract	Decision-analytic models must often be informed using data which are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters, and how those parameters are related to quantities of interest for decision-making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data, and at the same time, propagates all parameter uncertainties to the results used for decision-making. To illustrate these methods, the authors demonstrate how a previously-developed Markov model for the progression of human papillomavirus (HPV16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV16 and HPV16-related disease by age, cervical cancer incidence, and other published information. Previously, a discrete collection of plausible scenarios was identified, but with no further indication of which of these are more plausible. Instead, the authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data. In particular, we emphasise the appropriate choice of prior distributions and checking and comparison of fitted models.
Related Links	http://dx.doi.org/10.1177/0272989x13493143
Ending Page	161
Page Count	14
Starting Page	148
File Format	PDF
ISSN	0272989X
e-ISSN	1552681X
Journal	Medical decision making : an international journal of the Society for Medical Decision Making
Issue Number	2
Volume Number	35
Language	English
Publisher Date	2015-02-01
Access Restriction	Open
Subject Keyword	Health Policy Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Health Policy

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