Calibration of disease simulation model using an engineering approach.
Calibration of disease simulation model using an engineering approach.
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DOI：
10.1111/j.1524-4733.2008.00484.x
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发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
Gazelle GS
中科院分区：
文献类型：
--
作者：
Kong CY;McMahon PM;Gazelle GS
根据现有临床数据校准疾病模拟模型的输出对于建立对模型预测能力的信心至关重要。校准涉及两个挑战：1）如果需要同时拟合，则定义多个目标的总拟合优度分数;以及2）搜索使总拟合优度分数最小化的最佳参数集（即，产生最佳拟合）。为了解决这两个突出的挑战，我们应用了一种工程方法来校准微观模拟模型，肺癌政策模型（LCPM）。首先，来自临床和流行病学数据的11个目标被组合成一个总的拟合优度得分的加权和的方法，占用户定义的相对重要性的校准目标。第二，两个自动参数搜索算法，模拟退火（SA）和遗传算法（GA），独立地应用于同时搜索的28个自然历史参数，以尽量减少总的拟合优度得分。针对速度和模型拟合定义了算法性能指标。这两种搜索算法在1,000次搜索迭代中获得的总拟合优度得分均低于95。我们的研究结果表明，SA优于GA定位较低的拟合优度。在校准我们的LCPM后，预测的肺癌自然史与其他肺癌发展的数学模型一致。一种基于工程的校准方法能够同时将LCPM输出拟合到多个校准目标，具有计算速度快、减少人工输入需求及其潜在偏差的优点。
Calibrating a disease simulation model’s outputs to existing clinical data is vital to generate confidence in the model’s predictive ability. Calibration involves two challenges: 1) defining a total goodness-of-fit score for multiple targets if simultaneous fitting is required; and 2) searching for the optimal parameter set that minimizes the total goodness-of-fit score (i.e., yields the best fit). To address these two prominent challenges, we have applied an engineering approach to calibrate a microsimulation model, the Lung Cancer Policy Model (LCPM). First, eleven targets derived from clinical and epidemiological data were combined into a total goodness-of-fit score by a weighted-sum approach, accounting for the user-defined relative importance of the calibration targets. Second, two automated parameter search algorithms, Simulated Annealing (SA) and Genetic Algorithm (GA), were independently applied to a simultaneous search of 28 natural history parameters to minimize the total goodness-of-fit score. Algorithm performance metrics were defined for speed and model fit. Both search algorithms obtained total goodness-of-fit scores below 95 within 1,000 search iterations. Our results show that SA outperformed GA in locating a lower goodness-of-fit. After calibrating our LCPM, the predicted natural history of lung cancer was consistent with other mathematical models of lung cancer development. An engineering-based calibration method was able to simultaneously fit LCPM output to multiple calibration targets, with the benefits of fast computational speed and reduced need for human input and its potential bias.
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影响因子：
39.2
作者：
Goldie, SJ;Weinstein, MC;Freedberg, KA
通讯作者：
Freedberg, KA
影响因子：
8.8
作者：
Damber LA;Larsson LG
通讯作者：
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影响因子：
2.1
作者：
Clements, MS;Armstrong, BK;Moolgavkar, SH
通讯作者：
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影响因子：
1.9
作者：
HOLFORD, TR
通讯作者：
HOLFORD, TR
影响因子：
3.6
作者：
McMahon, Pamela M.;Zaslavsky, Alan M.;Gazelle, G. Scott
通讯作者：
Gazelle, G. Scott