Abstract:
:In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC(50)). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty.
journal_name
Biometricsjournal_title
Biometricsauthors
Hennessey VG,Rosner GL,Bast RC Jr,Chen MYdoi
10.1111/j.1541-0420.2010.01403.xsubject
Has Abstractpub_date
2010-12-01 00:00:00pages
1275-83issue
4eissn
0006-341Xissn
1541-0420pii
BIOM1403journal_volume
66pub_type
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