Experimental designs for drug combination studies

Bader Almohaimeed, A. N. Donev

    Research output: Contribution to journalArticlepeer-review


    The interest in drug combinations is growing rapidly due to the opportunities they create to increase the therapeutic effect and to reduce the frequency or magnitude of undesirable side effects when single drugs fail to deliver satisfactory results. Considerable effort in studying benefits of the joint action of drugs has been matched by the development of relevant statistical methods and tools for statistical analysis of the data obtained in such studies that allow important statistical assumptions to be taken into account, i.e. the appropriate statistical model and the distribution of the response of interest (e.g. Gaussian, Binomial, Poisson). However, much less attention has been given to the choice of suitable experimental designs for such studies, while only high quality data can ensure that the objectives of the studies will be fulfilled. Methods for construction of such experimental designs which are economical and make most efficient use of the available resources are proposed. It is shown how this can be performed when the distribution of the response is one of those belonging to the exponential family of distributions, and provide specific examples for the most common cases. In addition simple but flexible experimental designs, called ray-contour designs, are proposed. These designs are particularly useful when the use of low or high doses is undesirable and hence a standard statistical analysis of the data is not possible. Useful features of these designs are illustrated with an application in cancer study. © 2013 Published by Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)1077-1087
    Number of pages10
    JournalComputational Statistics and Data Analysis
    Early online date18 Jan 2013
    Publication statusPublished - 2014


    • Additivity
    • Antagonism
    • Ray design
    • Ray-contour design
    • Synergy


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