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Sequential methods for pharmacogenetic studies

Author

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  • Todd, Susan
  • Fazil Baksh, M.
  • Whitehead, John

Abstract

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.

Suggested Citation

  • Todd, Susan & Fazil Baksh, M. & Whitehead, John, 2012. "Sequential methods for pharmacogenetic studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1221-1231.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1221-1231
    DOI: 10.1016/j.csda.2011.02.019
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    References listed on IDEAS

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    1. Cui, Yin & Fu, Yuejiao & Hussein, Abdulkadir, 2009. "Group sequential testing of homogeneity in genetic linkage analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3630-3639, August.
    2. Karvanen, Juha & Kulathinal, Sangita & Gasbarra, Dario, 2009. "Optimal designs to select individuals for genotyping conditional on observed binary or survival outcomes and non-genetic covariates," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1782-1793, March.
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