RT Journal Article
SR Electronic
T1 Multivariate Analysis of PET Pharmacokinetic Parameters
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.04.490593
DO 10.1101/2022.05.04.490593
A1 Granville J. Matheson
A1 R. Todd Ogden
YR 2022
UL http://biorxiv.org/content/early/2022/05/04/2022.05.04.490593.abstract
AB Purpose In positron emission tomography (PET) 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.Methods 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 PuMBA (Parameters undergoing Multivariate Bayesian Analysis). We simulated patientcontrol 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.Results 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.Conclusion 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.Competing Interest StatementThe authors have declared no competing interest.