Nonlinear Mixed-Effects Models for PET Data
Nonlinear Mixed-Effects Models for PET Data
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DOI：
10.1109/tbme.2018.2861875
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发表时间：
2019-03-01
影响因子：
4.6
通讯作者：
Ogden, R. Todd
中科院分区：
文献类型：
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作者：
Chen, Yakuan;Goldsmith, Jeff;Ogden, R. Todd
目的：目前用于动态PET数据的隔室建模的最新技术可以描述为两个阶段的方法。在第一阶段，通过使用标准技术(如非线性最小二乘法)将模型与每个个体的数据进行拟合来获得动力学参数的个体估计，每次一个受试者。在第二阶段使用标准统计方法分析总体水平的影响，例如诊断组之间的差异，将个体估计视为观察数据。虽然这种方法通常是有效的，但有可能提高分析的效率和精度，允许拟合更复杂的模型，还可以通过同时拟合不同受试者的数据来进行特定参数的调查。在这种背景下，我们探索了非线性混合效应(NLME)模型在估计和推断中的应用。方法：在NLME框架中，通过包含受试者对每个动力学参数的随机效应来同时对受试者进行建模；同时，在联合模型中直接估计总体参数。结果：模拟结果表明，NLME在估计组水平效应方面优于两阶段法，并且具有更好的跨组差异检测能力。我们将我们的NLME方法应用于临床PET数据，发现两阶段方法没有检测到的效果。结论：在PET数据的房室模型中，所提出的NLME方法比两阶段方法更准确，相应地更强大。意义：由于NLME方法的有效性和稳定性，它可以拓宽PET建模的方法学范围。
Objective: The current state-of-the-art for compartment modeling of dynamic PET data can be described as a two-stage approach. In Stage 1, individual estimates of kinetic parameters are obtained by fitting models using standard techniques, such as nonlinear least squares, to each individual's data one subject at a time. Population-level effects, such as the difference between diagnostic groups, are analyzed in Stage 2 using standard statistical methods by treating the individual estimates as if they were observed data. While this approach is generally valid, it is possible to increase efficiency and precision of the analysis, allow more complex models to be fitted, and also to permit parameter-specific investigation by fitting data across subjects simultaneously. We explore the application of nonlinear mixed-effects (NLME) models for estimation and inference in this setting. Methods: In the NLME framework, subjects are modeled simultaneously through the inclusion of random effects of subjects for each kinetic parameter; meanwhile, population parameters are estimated directly in a joint model. Results: Simulation results indicate that NLME outperforms the two-stage approach in estimating group-level effects and also has improved power to detect differences across groups. We applied our NLME approach to clinical PET data and found effects not detected by the two-stage approach. Conclusion: The proposed NLME approach is more accurate and correspondingly more powerful than the two-stage approach in compartment modeling of PET data. Significance: The NLME method can broaden the methodological scope of PET modeling because of its efficiency and stability.