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Drug Combination Studies, Uniform Experimental Design and Extensions

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Contemporary Experimental Design, Multivariate Analysis and Data Mining

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Abstract

Drug combination has been an important therapeutic development approach for cancer, viral or microbial infections, and other diseases involving complex biological networks. Synergistic drug combinations, which are more effective than predicted from summing effects of individual drugs, often achieve improved therapeutic index. Because drug-effect is dose-dependent, multiple doses of an individual drug need to be evaluated, giving rapidly escalating number of combinations and a challenging high dimensional statistical modeling problem. The lack of proper design and analysis methods for multi-drug combination studies have resulted in many missed therapeutic opportunities. It is known that, in the presence of model uncertainties, uniform measures that scatter the design points (the dose levels) uniformly in the experiment domain is the best strategy to yield maximum information on the dose response relation. This chapter will review some efficient experimental designs for drug combination studies especially those related to uniform measures and extensions using maximum entropy design.

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Acknowledgements

This research is partially supported by the National Cancer Institute (NCI) grant R01CA164717.

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Authors and Affiliations

  1. Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC, 20057, USA

    Ming T. Tan & Hong-Bin Fang

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  1. Ming T. Tan
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  2. Hong-Bin Fang
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Correspondence to Ming T. Tan .

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Editors and Affiliations

  1. Department of Financial Engineering, Princeton University, Princeton, NJ, USA

    Jianqing Fan

  2. Department of Mathematics, The University of Manchester, Manchester, UK

    Jianxin Pan

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Tan, M.T., Fang, HB. (2020). Drug Combination Studies, Uniform Experimental Design and Extensions. In: Fan, J., Pan, J. (eds) Contemporary Experimental Design, Multivariate Analysis and Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-030-46161-4_8

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