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Activity Number: 180 - Contributed Poster Presentations: Section on Teaching of Statistics in the Health Sciences
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Teaching of Statistics in the Health Sciences
Abstract #324259
Title: Parametric and Nonparametric Approaches for Tumor Growth Data
Author(s): Bingsong Zhang* and Valeriy Korostyshevskiy
Companies: Georgetown University and Georgetown University
Keywords: MANOVA ; Non-parametric model ; Gompertzian ; Linear Regression
Abstract:

We review a set of parametric and non-parametric methods that are widely used in longitudinal data analysis, and illustrate their application with data from a published study of effect of alpha-difluoromethyl-ornithine (DFMO) on the growth of BT-20 human breast tumors in nude mice. Three parametric methods find significant difference between DFMO dose groups, while the non-parametric method shows that all groups follow a similar growth pattern, which provides reasonable support to fit a single mathematical model with different parameters. We find a correlation between model parameters and dosage, also another correlation between parameters and initial tumor volume is found in pool data. We conclude that by taking such two correlations into consideration, we can predict future tumor growth given initial tumor volume and dosage applied.


Authors who are presenting talks have a * after their name.

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