Rutter Carolyn M, Miglioretti Diana L, Savarino James E
Carolyn M. Rutter is Senior Investigator, Group Health Center for Health Studies, Seattle, WA 98101 (
J Am Stat Assoc. 2009 Dec 1;104(488):1338-1350. doi: 10.1198/jasa.2009.ap07466.
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.
描述疾病进程的微观模拟模型整合了来自多个来源的信息，可用于估计筛查和治疗对人群层面癌症发病率和死亡率的影响。这些模型的特点是对感兴趣的理想化人群的个体事件历史进行模拟。微观模拟模型很复杂，总是包含一些现有数据无法充分提供信息的参数。因此，模型开发的一个关键组成部分是参数值的选择。微观模拟模型的参数值是通过模型校准过程来选择的，以重现预期或已知的结果。校准可以通过一次扰动一个模型参数或使用搜索算法来完成。作为一种替代方法，我们提出一种使用马尔可夫链蒙特卡罗方法来校准微观模拟模型的贝叶斯方法。我们证明这种方法收敛于目标分布，并通过模拟研究来展示其有限样本性能。虽然这种方法计算量很大，但与先前提出的方法相比有几个优点，包括使用统计标准来选择参数值、将多个参数同时校准到多个数据源、通过先验分布纳入信息、描述参数可识别性以及能够获得模型参数的区间估计。我们开发了一个用于结直肠癌的微观模拟模型，并使用我们提出的方法来校准模型参数。该微观模拟模型与校准数据拟合良好。我们发现有证据表明一些参数主要是通过先验分布来识别的。我们的结果强调了在校准和应用微观模拟模型时纳入多种变异性来源（即由于校准数据、未知参数、估计参数和预测值）的必要性。
