DNA microarrays are an important technology for studying gene expression. With a single hybridization, the level of expression of thousands of genes, or even an entire genome, can be estimated for a sample of cells. Consequently, manylaboratoriesareattemptingtoutilizeDNAmicroarraysintheirresearch. Whereaslaboratoriesarewellpreparedtoaddressthesigni?cantexperimental challenges in obtaining reproducible data from this RNA-based assay, inv- tigators are less prepared to analyze the large volumes of data produced by DNA microarrays. Although many software packages have been developed for the analysis of DNA microarray data, software alone is insu?cient. One needs knowledge aboutthevariousaspectsofdataanalysisinordertoselectandutilizesoftware e?ectively. There is a plethora of analysis methods being published and it is di?cult for biologists to determine which methods are valid and appropriate for their problems. Many scientists have learned that software is not an adequate substitute for biostatistical knowledge and seek statistical collaborators. Unfortunately, there is presently a shortage of statisticians who are available and knowled- able about DNA microarrays. For statisticians to be e?ective collaborators in anyarea,theymustinvestthetimetounderstandthesubjectmatterareaand become familiar with the literature so that they can ask the right questions and identify the key issues. Our objectives in this book are twofold: to provide scientists with infor- tion about the design and analysis of studies using DNA microarrays that will enable them to plan and analyze their own studies or to work with statistical collaborators e?ectively, and to aid statistical and computational scientists wishing to develop expertise in this area.

DNA microarrays are an important technology for studying gene expression. With a single hybridization, the level of expression of thousands of genes, or even an entire genome, can be estimated for a sample of cells. Consequently, manylaboratoriesareattemptingtoutilizeDNAmicroarraysintheirresearch. Whereaslaboratoriesarewellpreparedtoaddressthesigni?cantexperimental challenges in obtaining reproducible data from this RNA-based assay, inv- tigators are less prepared to analyze the large volumes of data produced by DNA microarrays. Although many software packages have been developed for the analysis of DNA microarray data, software alone is insu?cient. One needs knowledge aboutthevariousaspectsofdataanalysisinordertoselectandutilizesoftware e?ectively. There is a plethora of analysis methods being published and it is di?cult for biologists to determine which methods are valid and appropriate for their problems. Many scientists have learned that software is not an adequate substitute for biostatistical knowledge and seek statistical collaborators. Unfortunately, there is presently a shortage of statisticians who are available and knowled- able about DNA microarrays. For statisticians to be e?ective collaborators in anyarea,theymustinvestthetimetounderstandthesubjectmatterareaand become familiar with the literature so that they can ask the right questions and identify the key issues. Our objectives in this book are twofold: to provide scientists with infor- tion about the design and analysis of studies using DNA microarrays that will enable them to plan and analyze their own studies or to work with statistical collaborators e?ectively, and to aid statistical and computational scientists wishing to develop expertise in this area.

Genomics is majorly impacting therapeutics development in medicine. This book contains up-to-date information on the use of genomics in the design and analysis of therapeutic clinical trials with a focus on novel approaches that provide a reliable basis for identifying which patients are most likely to benefit from each treatment. It is oriented to both clinical investigators and statisticians. For clinical investigators, it includes background information on clinical trial design and statistical analysis. For statisticians and others who want to go deeper, it covers state-of-the-art adaptive designs and the development and validation of probabilistic classifiers. The author describes the development and validation of prognostic and predictive biomarkers and their integration into clinical trials that establish their clinical utility for informing treatment decisions for future patients.

Genomics is majorly impacting therapeutics development in medicine. This book contains up-to-date information on the use of genomics in the design and analysis of therapeutic clinical trials with a focus on novel approaches that provide a reliable basis for identifying which patients are most likely to benefit from each treatment. It is oriented to both clinical investigators and statisticians. For clinical investigators, it includes background information on clinical trial design and statistical analysis. For statisticians and others who want to go deeper, it covers state-of-the-art adaptive designs and the development and validation of probabilistic classifiers. The author describes the development and validation of prognostic and predictive biomarkers and their integration into clinical trials that establish their clinical utility for informing treatment decisions for future patients.

DNA microarrays are an important technology for studying gene expression. With a single hybridization, the level of expression of thousands of genes, or even an entire genome, can be estimated for a sample of cells. Consequently, manylaboratoriesareattemptingtoutilizeDNAmicroarraysintheirresearch. Whereaslaboratoriesarewellpreparedtoaddressthesigni?cantexperimental challenges in obtaining reproducible data from this RNA-based assay, inv- tigators are less prepared to analyze the large volumes of data produced by DNA microarrays. Although many software packages have been developed for the analysis of DNA microarray data, software alone is insu?cient. One needs knowledge aboutthevariousaspectsofdataanalysisinordertoselectandutilizesoftware e?ectively. There is a plethora of analysis methods being published and it is di?cult for biologists to determine which methods are valid and appropriate for their problems. Many scientists have learned that software is not an adequate substitute for biostatistical knowledge and seek statistical collaborators. Unfortunately, there is presently a shortage of statisticians who are available and knowled- able about DNA microarrays. For statisticians to be e?ective collaborators in anyarea,theymustinvestthetimetounderstandthesubjectmatterareaand become familiar with the literature so that they can ask the right questions and identify the key issues. Our objectives in this book are twofold: to provide scientists with infor- tion about the design and analysis of studies using DNA microarrays that will enable them to plan and analyze their own studies or to work with statistical collaborators e?ectively, and to aid statistical and computational scientists wishing to develop expertise in this area.

DNA microarrays are an important technology for studying gene expression. With a single hybridization, the level of expression of thousands of genes, or even an entire genome, can be estimated for a sample of cells. Consequently, manylaboratoriesareattemptingtoutilizeDNAmicroarraysintheirresearch. Whereaslaboratoriesarewellpreparedtoaddressthesigni?cantexperimental challenges in obtaining reproducible data from this RNA-based assay, inv- tigators are less prepared to analyze the large volumes of data produced by DNA microarrays. Although many software packages have been developed for the analysis of DNA microarray data, software alone is insu?cient. One needs knowledge aboutthevariousaspectsofdataanalysisinordertoselectandutilizesoftware e?ectively. There is a plethora of analysis methods being published and it is di?cult for biologists to determine which methods are valid and appropriate for their problems. Many scientists have learned that software is not an adequate substitute for biostatistical knowledge and seek statistical collaborators. Unfortunately, there is presently a shortage of statisticians who are available and knowled- able about DNA microarrays. For statisticians to be e?ective collaborators in anyarea,theymustinvestthetimetounderstandthesubjectmatterareaand become familiar with the literature so that they can ask the right questions and identify the key issues. Our objectives in this book are twofold: to provide scientists with infor- tion about the design and analysis of studies using DNA microarrays that will enable them to plan and analyze their own studies or to work with statistical collaborators e?ectively, and to aid statistical and computational scientists wishing to develop expertise in this area.

Genomics is majorly impacting therapeutics development in medicine. This book contains up-to-date information on the use of genomics in the design and analysis of therapeutic clinical trials with a focus on novel approaches that provide a reliable basis for identifying which patients are most likely to benefit from each treatment. It is oriented to both clinical investigators and statisticians. For clinical investigators, it includes background information on clinical trial design and statistical analysis. For statisticians and others who want to go deeper, it covers state-of-the-art adaptive designs and the development and validation of probabilistic classifiers. The author describes the development and validation of prognostic and predictive biomarkers and their integration into clinical trials that establish their clinical utility for informing treatment decisions for future patients.

Genomics is majorly impacting therapeutics development in medicine. This book contains up-to-date information on the use of genomics in the design and analysis of therapeutic clinical trials with a focus on novel approaches that provide a reliable basis for identifying which patients are most likely to benefit from each treatment. It is oriented to both clinical investigators and statisticians. For clinical investigators, it includes background information on clinical trial design and statistical analysis. For statisticians and others who want to go deeper, it covers state-of-the-art adaptive designs and the development and validation of probabilistic classifiers. The author describes the development and validation of prognostic and predictive biomarkers and their integration into clinical trials that establish their clinical utility for informing treatment decisions for future patients.