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Summarizing EC50 estimates from multiple dose‐response experiments: A comparison of a meta‐analysis strategy to a mixed‐effects model approach
Author(s) -
Jiang Xiaoqi,
KoppSchneider Annette
Publication year - 2014
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201300123
Subject(s) - ec50 , statistics , automatic summarization , context (archaeology) , mixed model , mathematics , computer science , confidence interval , econometrics , artificial intelligence , paleontology , biochemistry , chemistry , in vitro , biology
Dose‐response studies are performed to investigate the potency of a compound. EC50 is the concentration of the compound that gives half‐maximal response. Dose‐response data are typically evaluated by using a log‐logistic model that includes EC50 as one of the model parameters. Often, more than one experiment is carried out to determine the EC50 value for a compound, requiring summarization of EC50 estimates from a series of experiments. In this context, mixed‐effects models are designed to estimate the average behavior of EC50 values over all experiments by considering the variabilities within and among experiments simultaneously. However, fitting nonlinear mixed‐effects models is more complicated than in a linear situation, and convergence problems are often encountered. An alternative strategy is the application of a meta‐analysis approach, which combines EC50 estimates obtained from separate log‐logistic model fitting. These two proposed strategies to summarize EC50 estimates from multiple experiments are compared in a simulation study and real data example. We conclude that the meta‐analysis strategy is a simple and robust method to summarize EC50 estimates from multiple experiments, especially suited in the case of a small number of experiments.