Modeling cancer cells growth

Research output: Contribution to journalArticle

Abstract

In molecular oncology the analysis of growth patterns of cells represent a major goal for understanding the evolution of growth profiles in pathological conditions. To compare various experimental settings in a pre-clinical study of inhibitors of prostate cancer, we modeled cancer cell growth using different statistical approaches which account for biological variability and complexity within experiments and over time. We first estimated cell growth by means of Linear Mixed Effects models that included unobserved factors that may influence cell development. Random effects were included to allow for no constant variance and covariance of residuals over time (Wu and West, 2009) and to properly model the whole growth process (i.e., all observation times). These are crucial aspects that are commonly ignored by the standard Linear Model. Since the nature of the data does not support the assumption of strictly linear effect of the continuous covariates on the predictor we present a solution within generalized additive models with mixed effects. An extension of this modeling to a fully Bayesian framework is also considered as it has a high degree of flexibility.

Original languageEnglish
Pages (from-to)3043-3059
Number of pages17
JournalCommunications in Statistics - Theory and Methods
Volume41
Issue number16-17
DOIs
Publication statusPublished - 2012

Keywords

  • Bayesian approach
  • Generalized additive models
  • Growth curve modeling
  • Linear mixed effects model

ASJC Scopus subject areas

  • Statistics and Probability

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