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Pharmacogenomics is the co-development of a drug that targets a subgroup of the patients,and a device that predicts whether a patient is in the subgroup of responders to thedrug. It is a two-stage process, including a training stage and a validation stage. The purposeof the training stage is to identify a biomarker positive (G+) subgroup of patients andits complement, the biomarker negative (G-) subgroup. Typically, subgroups are discoveredby comparing the genetic profiles of the responders to the drug with the non-responders.Microarrays could be used to develop such a diagnostic device for identification of subgroups.The purpose of the validation stage is then to prove that the biomarker found in thetraining stage has sufficient sensitivity and specificity for clinical use, and to independentlyvalidate the efficacy and safety of the drug for the target G+ subgroup.Major statistical problems in the analysis of microarray experiments in pharmacogenomicsinclude normalization of gene expressions, biomarker selection in the training stageand determination of sample sizes for a validation study. Before doing any formal analysison gene expression data, it is important to normalize the data first to reduce variation betweenarrays caused by sources of non-biological origin. Then for biomarker selection inthe training stage, a re-sampling based multiple testing procedure is proposed by followingthe generalized partitioning principles. This procedure controls generalized FamilywiseError Rates (gFWER) asymptotically. To plan for a validation study, sample sizes for microarrayexperiments are determined to meet the pre-specified sensitivity and specificityrequirements.This dissertation is arranged as follows. Chapter 1 introduces the motivation of pharmacogenomicsand design considerations of microarray experiments in pharmacogenomics.Chapter 2 compares different normalization techniques for microarray experiments. Chapter3 focuses on the strong control of gFWER in multiple hypothesis testing. The resamplingbased multiple testing procedures are applied to select differentially expressedgenes in the training stage. Chapter 4 formulates sample size determination procedures forvalidation studies with change of platforms taken into account. Chapter 5 discusses futureresearch. |
