Med Decis Making - Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.

Tópicos

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Resumo

Decision-analytic models must often be informed using data that 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 (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16-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 emphasize the appropriate choice of prior distributions and checking and comparison of fitted models.

Resumo Limpo

decisionanalyt model must often inform use data indirect relat main model paramet author outlin implement bayesian synthesi divers sourc evid calibr paramet complex model graphic model built repres observ data generat statist model unknown paramet paramet relat quantiti interest decis make form basi algorithm estim posterior probabl distribut repres updat state evid unknown given data prior belief process calibr quantiti interest data time propag paramet uncertainti result use decis make illustr method author demonstr previous develop markov model progress human papillomavirus hpv infect rebuilt bayesian framework transit probabl state diseas sever infer indirect crosssect observ preval hpv hpvrelat diseas age cervic cancer incid publish inform previous discret collect plausibl scenario identifi indic plausibl instead author deriv bayesian posterior distribut scenario implicit weight accord well support data particular emphas appropri choic prior distribut check comparison fit model

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