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authorJohannes Ranke <jranke@uni-bremen.de>2021-09-16 15:31:13 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2021-09-16 17:36:26 +0200
commitc41381a961263c28d60976e68923157916c78b15 (patch)
tree8259a2dd4a374734a5521b9bd1b10dbdf8c39a0e /vignettes/references.bib
parente0ca5972b4a300b93a9fe6b44345eeb19d574149 (diff)
Adapt and improve the dimethenamid vignette
Adapt to the corrected data and unify control parameters for saemix and nlmixr with saem. Update docs
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}
+@Article{ranke2021,
+ AUTHOR = {Ranke, Johannes and Wöltjen, Janina and Schmidt, Jana and Comets, Emmanuelle},
+ TITLE = {Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models},
+ JOURNAL = {Environments},
+ VOLUME = {8},
+ YEAR = {2021},
+ NUMBER = {8},
+ ARTICLE-NUMBER = {71},
+ URL = {https://www.mdpi.com/2076-3298/8/8/71},
+ ISSN = {2076-3298},
+ ABSTRACT = {When data on the degradation of a chemical substance have been collected in a number of environmental media (e.g., in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used. Here, we show the advantages of such simultaneous evaluations based on nonlinear mixed-effects models that incorporate such assumptions in the evaluation process. The advantages of this approach are demonstrated using synthetically generated data with known statistical properties and using publicly available experimental degradation data on two pesticidal active substances.},
+ DOI = {10.3390/environments8080071}
+}
@Article{gao11,
Title = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares},

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