Bayesian Calibration of Microsimulation Models.
Bayesian Calibration of Microsimulation Models.
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DOI：
10.1198/jasa.2009.ap07466
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发表时间：
2009-12-01
影响因子：
3.7
通讯作者：
Savarino JE
中科院分区：
文献类型：
--
作者：
Rutter CM;Miglioretti DL;Savarino JE
Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models.
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影响因子：
2.3
作者：
Feuer, EJ;Etzioni, R;Mariotto, A
通讯作者：
Mariotto, A
DOI：
10.1093/jnci/djn389
发表时间：
2008-12-03
期刊：
Journal of the National Cancer Institute
影响因子：
--
作者：
Jemal A;Thun MJ;Ries LA;Howe HL;Weir HK;Center MM;Ward E;Wu XC;Eheman C;Anderson R;Ajani UA;Kohler B;Edwards BK
通讯作者：
Edwards BK
影响因子：
10.3
作者：
HIXSON, LJ;FENNERTY, MB;GAREWAL, H
通讯作者：
GAREWAL, H
影响因子：
2.4
作者：
Brooks, SP;Gelman, A
通讯作者：
Gelman, A
影响因子：
1.9
作者：
Garrett, ES;Zeger, SL
通讯作者：
Zeger, SL