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Bayesian Methods for Identifying Genetic Predictors in Smoking Cessation Studies

Bayesian Methods for Identifying Genetic Predictors in Smoking Cessation Studies


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About the Book

In clinical trials and observational studies, investigators may seek to identify genetic markers that are associated with treatment outcome, either directly or through interactions with treatments. Identification of such markers can help in elucidating the biological mechanisms and in identifying optimal treatments for individual subjects. A common approach is to screen all the markers using single-marker models and designate for further analysis those that are significantly associated with the outcome. When the number of potential markers is large, failure to adjust for multiplicity may lead to excess false positives. A range of recently developed frequentist approaches adjust properly for multiplicity but may be inefficient in the sense of failing to use available prior information. Moreover methods that select best models from all the markers simultaneously, rather than one marker at a time, would be preferable as sources of basic biological and clinical information. In this dissertation, we propose the use of Bayesian procedures to detect important markers. Our first project uses the Bayes factor (BF) to screen all the single-marker models and identify a subset of markers of potential interest. Our second project develops an approach to maintain the efficiency of BF tests while controlling the overall type I error rate, using a method we call the Multiplicity-Calibrated Bayesian Hypothesis Testing (MCBHT) procedure. In our third project, we develop a comprehensive approach to select best models from all the markers simultaneously, using a Bayesian variable selection method that explicitly incorporates prior information in order to involve prior information and enforce parsimony. We use simulations to demonstrate the power and value of our methods. We apply the methods to the analysis of two smoking cessation datasets, thereby identifying a small set of plausibly predictive genetic markers.


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Product Details
  • ISBN-13: 9781243619891
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 246 mm
  • Weight: 191 gr
  • ISBN-10: 1243619899
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Spine Width: 5 mm
  • Width: 189 mm


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