دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting.
المؤلفون: Oberg AL; Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. oberg.ann@mayo.edu., Heinzen EP; Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA., Hou X; Division of Medical Oncology, Department of Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA., Al Hilli MM; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.; Division of Subspecialty Care for Women's Health, Gynecologic Oncology, Women's Health Institute, Cleveland Clinic, Cleveland, OH, 44126, USA., Hurley RM; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.; Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA., Wahner Hendrickson AE; Division of Medical Oncology, Department of Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA., Goergen KM; Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA., Larson MC; Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA., Becker MA; Division of Medical Oncology, Department of Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.; Blueprint Medicines, 45 Sidney St., Cambridge, MA, 02139, USA., Eckel-Passow JE; Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA., Maurer MJ; Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA., Kaufmann SH; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.; Division of Oncology Research, Department of Oncology, Mayo Clinic, Rochester, 55905, MN, USA., Haluska P; Division of Medical Oncology, Department of Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.; Bristol-Meyers Squibb, 3401 Princeton Pike, Lawrenceville, NJ, 08648, USA., Weroha SJ; Division of Medical Oncology, Department of Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
المصدر: Scientific reports [Sci Rep] 2021 Apr 13; Vol. 11 (1), pp. 8076. Date of Electronic Publication: 2021 Apr 13.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Ovarian Neoplasms*/pathology , Xenograft Model Antitumor Assays*, Humans ; Female ; Animals ; Mice ; Tumor Burden ; Linear Models
مستخلص: 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.
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معلومات مُعتمدة: P50 CA108961 United States CA NCI NIH HHS; T32 GM065841 United States GM NIGMS NIH HHS; R01 CA184502 United States CA NCI NIH HHS; P30 CA015083 United States CA NCI NIH HHS; P50 CA136393 United States CA NCI NIH HHS
تواريخ الأحداث: Date Created: 20210414 Date Completed: 20240724 Latest Revision: 20240724
رمز التحديث: 20240726
مُعرف محوري في PubMed: PMC8044116
DOI: 10.1038/s41598-021-87470-x
PMID: 33850213
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-021-87470-x