Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting

Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling of tumor growth data. Biologically plausible...

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Vydané v:Scientific reports Ročník 11; číslo 1; s. 8076 - 15
Hlavní autori: Oberg, Ann L., Heinzen, Ethan P., Hou, Xiaonan, Al Hilli, Mariam M., Hurley, Rachel M., Wahner Hendrickson, Andrea E., Goergen, Krista M., Larson, Melissa C., Becker, Marc A., Eckel-Passow, Jeanette E., Maurer, Matthew J., Kaufmann, Scott H., Haluska, Paul, Weroha, S. John
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 13.04.2021
Nature Publishing Group
Nature Portfolio
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631/67
692/4028
Animals
Cancer
Female
Humanities and Social Sciences
Humans
Linear Models
Mathematical models
Mice
multidisciplinary
Ovarian cancer
Ovarian Neoplasms - pathology
Regression analysis
Science
Science (multidisciplinary)
Statistical analysis
Statistical models
Tumor Burden
Tumors
Within-subjects design
Xenograft Model Antitumor Assays
Xenografts
ISSN:2045-2322, 2045-2322
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Shrnutí:Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling of tumor growth data. Biologically plausible structures for the covariation between repeated tumor burden measurements are explained. Graphical, tabular, and information criteria tools useful for choosing the mean model functional form and covariation structure are demonstrated in a Case Study of five PDX models comparing cancer treatments. Power calculations were performed via simulation. Linear mixed effects regression models applied to the natural log scale were shown to describe the observed data well. A straight growth function fit well for two PDX models. Three PDX models required quadratic or cubic polynomial (time squared or cubed) terms to describe delayed tumor regression or initial tumor growth followed by regression. Spatial(power), spatial(power) + RE, and RE covariance structures were found to be reasonable. Statistical power is shown as a function of sample size for different levels of variation. Linear mixed effects regression models provide a unified and flexible framework for analysis of PDX repeated measures data, use all available data, and allow estimation of tumor doubling time.
Bibliografia:ObjectType-Article-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-87470-x