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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
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Nature Portfolio
2021-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-87470-x |
| _version_ | 1818685000035336192 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T10:59:34Z |
| format | Article |
| id | doaj.art-ff6b9ed53489469fac350642c746a06e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T10:59:34Z |
| publishDate | 2021-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ff6b9ed53489469fac350642c746a06e2022-12-21T21:51:44ZengNature PortfolioScientific Reports2045-23222021-04-0111111510.1038/s41598-021-87470-xStatistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) settingAnn L. Oberg0Ethan P. Heinzen1Xiaonan Hou2Mariam M. Al Hilli3Rachel M. Hurley4Andrea E. Wahner Hendrickson5Krista M. Goergen6Melissa C. Larson7Marc A. Becker8Jeanette E. Eckel-Passow9Matthew J. Maurer10Scott H. Kaufmann11Paul Haluska12S. John Weroha13Division of Computational Biology, Department of Quantitative Health Sciences, Mayo ClinicDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo ClinicDivision of Medical Oncology, Department of Oncology, Mayo ClinicDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, Mayo ClinicDepartment of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicDivision of Medical Oncology, Department of Oncology, Mayo ClinicDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo ClinicDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo ClinicDivision of Medical Oncology, Department of Oncology, Mayo ClinicDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo ClinicDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo ClinicDepartment of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicDivision of Medical Oncology, Department of Oncology, Mayo ClinicDivision of Medical Oncology, Department of Oncology, Mayo ClinicAbstract 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.https://doi.org/10.1038/s41598-021-87470-x |
| spellingShingle | 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 Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-021-87470-x |
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