Sequential methods for pharmacogenetic studies

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Todd, S. orcid id iconORCID: https://orcid.org/0000-0002-9981-923X, Baksh, M. F. orcid id iconORCID: https://orcid.org/0000-0003-3107-8815 and Whitehead, J. (2012) Sequential methods for pharmacogenetic studies. Computational Statistics & Data Analysis, 56 (5). pp. 1221-1231. ISSN 0167-9473 doi: 10.1016/j.csda.2011.02.019

Abstract/Summary

A study or experiment can be described as sequential if its design includes one or more interim analyses at which it is possible to stop the study, having reached a definitive conclusion concerning the primary question of interest. The potential of the sequential study to terminate earlier than the equivalent fixed sample size study means that, typically, there are ethical and economic advantages to be gained from using a sequential design. These advantages have secured a place for the methodology in the conduct of many clinical trials of novel therapies. Recently, there has been increasing interest in pharmacogenetics: the study of how DNA variation in the human genome affects the safety and efficacy of drugs. The potential for using sequential methodology in pharmacogenetic studies is considered and the conduct of candidate gene association studies, family-based designs and genome-wide association studies within the sequential setting is explored. The objective is to provide a unified framework for the conduct of these types of studies as sequential designs and hence allow experimenters to consider using sequential methodology in their future pharmacogenetic studies.

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Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/22429
Identification Number/DOI 10.1016/j.csda.2011.02.019
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
Life Sciences > School of Chemistry, Food and Pharmacy > School of Pharmacy > Pharmaceutics Research Group
Uncontrolled Keywords Association studies; Candidate gene; Genome-wide; Group-sequential; Interim analyses
Publisher Elsevier
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