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Action following the discovery of a global association between the whole genome and adverse event risk in a clinical drug-development programme

Whitehead, J., Kelly, P., Zhou, Y., Stallard, N., Thygesen, H. and Bowman, C. (2009) Action following the discovery of a global association between the whole genome and adverse event risk in a clinical drug-development programme. Pharmaceutical Statistics, 8 (4). pp. 287-300. ISSN 1539-1612

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To link to this article DOI: 10.1002/pst.357

Abstract/Summary

Observation of adverse drug reactions during drug development can cause closure of the whole programme. However, if association between the genotype and the risk of an adverse event is discovered, then it might suffice to exclude patients of certain genotypes from future recruitment. Various sequential and non-sequential procedures are available to identify an association between the whole genome, or at least a portion of it, and the incidence of adverse events. In this paper we start with a suspected association between the genotype and the risk of an adverse event and suppose that the genetic subgroups with elevated risk can be identified. Our focus is determination of whether the patients identified as being at risk should be excluded from further studies of the drug. We propose using a utility function to? determine the appropriate action, taking into account the relative costs of suffering an adverse reaction and of failing to alleviate the patient's disease. Two illustrative examples are presented, one comparing patients who suffer from an adverse event with contemporary patients who do not, and the other making use of a reference control group. We also illustrate two classification methods, LASSO and CART, for identifying patients at risk, but we stress that any appropriate classification method could be used in conjunction with the proposed utility function. Our emphasis is on determining the action to take rather than on providing definitive evidence of an association. Copyright (C) 2008 John Wiley & Sons, Ltd.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:9725
Uncontrolled Keywords:decision procedure, pharmacogenetics, pharmacovigilance, safety, monitoring, utility, TRUNCATED PRODUCT, RANDOM FOREST, P-VALUES, REGRESSION, SELECTION, CLASSIFICATION, ALGORITHM

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