Edward L. Korn – författare

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.

How to apply statistical methods to survey data--a guide toeffective analysis of health surveys. With large health surveys becoming increasingly available forpublic use, researchers with little experience in survey methodsare often faced with analyzing data from surveys to addressscientific and programmatic questions. This practical book providesstatistical techniques for use in survey analysis, making healthsurveys accessible to statisticians, biostatisticians,epidemiologists, and health researchers. The authors clearlyexplain the theory and methods of survey analysis along withreal-world applications. They draw on their work at the NationalInstitutes of Health as well as up-to-date information from acrossthe literature to present:* The sampling background necessary to understand health surveys.* The application of such techniques as t-tests, linear regression,logistic regression, and survival analysis to survey data.* The use of sample weights in survey data analysis.* Dealing with complications in variance estimation in large healthsurveys.* Applications involving cross-sectional, longitudinal, andmultiple cross-sectional surveys, and the use of surveys to performpopulation- based case-control analyses.* Guidance on the correct use of statistical methods found insoftware packages.* Extensive bibliography.

How to apply statistical methods to survey data--a guide toeffective analysis of health surveys. With large health surveys becoming increasingly available forpublic use, researchers with little experience in survey methodsare often faced with analyzing data from surveys to addressscientific and programmatic questions. This practical book providesstatistical techniques for use in survey analysis, making healthsurveys accessible to statisticians, biostatisticians,epidemiologists, and health researchers. The authors clearlyexplain the theory and methods of survey analysis along withreal-world applications. They draw on their work at the NationalInstitutes of Health as well as up-to-date information from acrossthe literature to present: * The sampling background necessary to understand health surveys. * The application of such techniques as t-tests, linear regression,logistic regression, and survival analysis to survey data. * The use of sample weights in survey data analysis. * Dealing with complications in variance estimation in large healthsurveys. * Applications involving cross-sectional, longitudinal, andmultiple cross-sectional surveys, and the use of surveys to performpopulation- based case-control analyses. * Guidance on the correct use of statistical methods found insoftware packages. * Extensive bibliography.