Multivariate analysis of PET pharmacokinetic parameters improves inferential efficiency.
Multivariate analysis of PET pharmacokinetic parameters improves inferential efficiency.
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DOI：
10.1186/s40658-023-00537-8
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发表时间：
2023-03-13
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影响因子：
4
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中科院分区：
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在正电子发射断层扫描定量中，通常从每个时间活性曲线估计多个药代动力学参数。传统上，在执行后续统计分析之前，除了感兴趣的参数之外的所有参数都被丢弃。然而，我们断言，这些丢弃的参数也包含相关的信息，可以利用这些信息来提高对感兴趣的参数的统计分析的精度和能力。因此，适当考虑到这一点，就可以在不收集更多数据的情况下得出更有信息的结论。通过应用分层多因素多变量贝叶斯方法，可以一次分析所有区域的所有估计参数。我们将这种方法称为参数进行多变量贝叶斯分析（PuMBA）。我们用不同的放射性配体、不同的样本量和测量误差模拟患者对照研究，以探讨其性能，比较相对于单变量分析方法的精密度、统计功效、假阳性率和估计组间差异的偏倚。我们表明，PuMBA提高了所有检查的应用程序相对于单变量方法的统计能力，而不增加假阳性率。PuMBA提高了效应量估计的精度，并减少了这些估计在模拟样本之间的变化。此外，我们表明，PuMBA产生的性能改善，即使在存在大量的测量误差。值得注意的是，由于其能够利用药代动力学参数之间共享的信息，PuMBA甚至显示出比模拟参数的真实结合值的常规单变量分析更大的功效。在所有应用程序中，PuMBA在估计结果中表现出较小程度的偏倚;然而，相对于模拟数据集之间估计结果的变化，这是很小的。PuMBA提高了PET数据统计分析的精度和能力，而无需收集额外的测量结果。这使得有可能在新的和以前收集的数据中研究新的研究问题。因此，PuMBA在PET成像领域具有很大的前景。
In positron emission tomography quantification, multiple pharmacokinetic parameters are typically estimated from each time activity curve. Conventionally all but the parameter of interest are discarded before performing subsequent statistical analysis. However, we assert that these discarded parameters also contain relevant information which can be exploited to improve the precision and power of statistical analyses on the parameter of interest. Properly taking this into account can thereby draw more informative conclusions without collecting more data. By applying a hierarchical multifactor multivariate Bayesian approach, all estimated parameters from all regions can be analysed at once. We refer to this method as Parameters undergoing Multivariate Bayesian Analysis (PuMBA). We simulated patient–control studies with different radioligands, varying sample sizes and measurement error to explore its performance, comparing the precision, statistical power, false positive rate and bias of estimated group differences relative to univariate analysis methods. We show that PuMBA improves the statistical power for all examined applications relative to univariate methods without increasing the false positive rate. PuMBA improves the precision of effect size estimation, and reduces the variation of these estimates between simulated samples. Furthermore, we show that PuMBA yields performance improvements even in the presence of substantial measurement error. Remarkably, owing to its ability to leverage information shared between pharmacokinetic parameters, PuMBA even shows greater power than conventional univariate analysis of the true binding values from which the parameters were simulated. Across all applications, PuMBA exhibited a small degree of bias in the estimated outcomes; however, this was small relative to the variation in estimated outcomes between simulated datasets. PuMBA improves the precision and power of statistical analysis of PET data without requiring the collection of additional measurements. This makes it possible to study new research questions in both new and previously collected data. PuMBA therefore holds great promise for the field of PET imaging.
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影响因子：
6.3
作者：
Ogden, R. Todd;Ojha, Ashish;Parsey, Ramin V.
通讯作者：
Parsey, Ramin V.
影响因子：
5.7
作者：
Lammertsma, AA;Hume, SP
通讯作者：
Hume, SP
影响因子：
2.3
作者：
Miller, Jeffrey M.;Zanderigo, Francesca;Mann, J. John
通讯作者：
Mann, J. John
DOI：
10.1097/00004647-200106000-00002
发表时间：
2001-06-01
影响因子：
6.3
作者：
Gunn, RN;Gunn, SR;Cunningham, VJ
通讯作者：
Cunningham, VJ
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen