Literature DB >> 15051414

Modeling antitumor activity by using a non-linear mixed-effects model.

Hua Liang1, Naijun Sha.   

Abstract

The response of solid tumors to antitumor treatment generally declines markedly with treatment time. Sometimes, a tumor regrows (rebounds) before the end of the treatment period. Studies of the patterns of tumor response to treatment are important, because they may provide useful information for clinical decision-making. We have investigated patterns of tumor response in mouse xenograft tumors by using data from a study conducted at St. Jude Children's Research Hospital. We applied a biexponential non-linear mixed-effects model to an analysis of changes in tumor volume over a given period of treatment. The model gives a good fit to the data, even for small sample sizes. We addressed the relation between the baseline tumor volumes and the decay rates of the first and second stages of the tumor's response to treatment, and we applied sensitive analysis to determine the effect of using different imputed values for missing data. We also proposed a novel approach to a comparison of the antitumor effects of three different treatments, and we used the data from a St. Jude study to demonstrate the potential of this comparison approach in cancer clinical decision-making.

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Year:  2004        PMID: 15051414     DOI: 10.1016/j.mbs.2004.01.002

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  5 in total

1.  Assessment of antitumor activity for tumor xenograft studies using exponential growth models.

Authors:  Jianrong Wu
Journal:  J Biopharm Stat       Date:  2011-05       Impact factor: 1.051

2.  A maximum Likelihood Approach to Analyzing Incomplete Longitudinal Data in Mammary Tumor Development Experiments with Mice.

Authors:  Jihnhee Yu; Albert Vexler; Alan D Hutson
Journal:  Sri Lankan J Appl Stat       Date:  2013-01-09

3.  Interval approach to assessing antitumor activity for tumor xenograft studies.

Authors:  Jianrong Wu; Peter J Houghton
Journal:  Pharm Stat       Date:  2010 Jan-Mar       Impact factor: 1.894

Review 4.  Growth rate analysis and efficient experimental design for tumor xenograft studies.

Authors:  Gregory Hather; Ray Liu; Syamala Bandi; Jerome Mettetal; Mark Manfredi; Wen-Chyi Shyu; Jill Donelan; Arijit Chakravarty
Journal:  Cancer Inform       Date:  2014-12-09

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

Authors:  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
Journal:  Sci Rep       Date:  2021-04-13       Impact factor: 4.379

  5 in total

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