Author:
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
Abstract
AbstractRepeated 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.
Funder
NCI
The Fred C. and Katherine B. Andersen Foundation
Mayo Foundation for Education and Research
NIH
Ovarian Cancer Research Fund
Publisher
Springer Science and Business Media LLC
Cited by
9 articles.
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