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

Abstract 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 p...

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Autores principales:	Ann L. Oberg, Ethan P. Heinzen, Xiaonan Hou, Mariam M. Al Hilli, Rachel M. Hurley, Andrea E. Wahner Hendrickson, Krista M. Goergen, Melissa C. Larson, Marc A. Becker, Jeanette E. Eckel-Passow, Matthew J. Maurer, Scott H. Kaufmann, Paul Haluska, S. John Weroha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ff6b9ed53489469fac350642c746a06e
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spelling	oai:doaj.org-article:ff6b9ed53489469fac350642c746a06e2021-12-02T14:30:46ZStatistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting10.1038/s41598-021-87470-x2045-2322https://doaj.org/article/ff6b9ed53489469fac350642c746a06e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87470-xhttps://doaj.org/toc/2045-2322Abstract 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.Ann L. ObergEthan P. HeinzenXiaonan HouMariam M. Al HilliRachel M. HurleyAndrea E. Wahner HendricksonKrista M. GoergenMelissa C. LarsonMarc A. BeckerJeanette E. Eckel-PassowMatthew J. MaurerScott H. KaufmannPaul HaluskaS. John WerohaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ann L. Oberg Ethan P. Heinzen Xiaonan Hou Mariam M. Al Hilli Rachel M. Hurley Andrea E. Wahner Hendrickson Krista M. Goergen Melissa C. Larson Marc A. Becker Jeanette E. Eckel-Passow Matthew J. Maurer Scott H. Kaufmann Paul Haluska S. John Weroha Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting
description	Abstract 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.
format	article
author	Ann L. Oberg Ethan P. Heinzen Xiaonan Hou Mariam M. Al Hilli Rachel M. Hurley Andrea E. Wahner Hendrickson Krista M. Goergen Melissa C. Larson Marc A. Becker Jeanette E. Eckel-Passow Matthew J. Maurer Scott H. Kaufmann Paul Haluska S. John Weroha
author_facet	Ann L. Oberg Ethan P. Heinzen Xiaonan Hou Mariam M. Al Hilli Rachel M. Hurley Andrea E. Wahner Hendrickson Krista M. Goergen Melissa C. Larson Marc A. Becker Jeanette E. Eckel-Passow Matthew J. Maurer Scott H. Kaufmann Paul Haluska S. John Weroha
author_sort	Ann L. Oberg
title	Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting
title_short	Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting
title_full	Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting
title_fullStr	Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting
title_full_unstemmed	Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting
title_sort	statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (pdx) setting
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ff6b9ed53489469fac350642c746a06e
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Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting

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