Methods In Multiple Testing And Meta Analysis With Applications To The Analysis Of Genomic Data
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Multiple Testing Procedures with Applications to Genomics
| Author | : Sandrine Dudoit |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 611 |
| Release | : 2007-12-18 |
| Genre | : Science |
| ISBN | : 0387493174 |
This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.
Meta-analysis and Combining Information in Genetics and Genomics
| Author | : Rudy Guerra |
| Publisher | : Chapman and Hall/CRC |
| Total Pages | : 360 |
| Release | : 2009-07-07 |
| Genre | : Mathematics |
| ISBN | : 9781584885221 |
Novel Techniques for Analyzing and Combining Data from Modern Biological Studies Broadens the Traditional Definition of Meta-Analysis With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective. After presenting the basic ideas and tools of meta-analysis, the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations. As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources.
Multiple Testing Procedures with Applications to Genomics
| Author | : Sandrine Dudoit |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2008-11-01 |
| Genre | : Science |
| ISBN | : 9780387517094 |
This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.
Meta-Analysis and Combining Information in Genetics and Genomics
| Author | : Rudy Guerra |
| Publisher | : Chapman & Hall/CRC |
| Total Pages | : 360 |
| Release | : 2017-06-14 |
| Genre | : |
| ISBN | : 9781138116115 |
Novel Techniques for Analyzing and Combining Data from Modern Biological Studies Broadens the Traditional Definition of Meta-Analysis With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, Meta-analysis and Combining Information in Genetics and Genomicslooks at how to analyze multiple studies from a broad perspective. After presenting the basic ideas and tools of meta-analysis, the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations. As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources.
Meta-Analysis
| Author | : Shahjahan Khan |
| Publisher | : Springer Nature |
| Total Pages | : 294 |
| Release | : 2020-10-27 |
| Genre | : Medical |
| ISBN | : 9811550328 |
This book focuses on performing hands-on meta-analysis using MetaXL, a free add-on to MS Excel. The illustrative examples are taken mainly from medical and health sciences studies, but the generic methods can be used to perform meta-analysis on data from any other discipline. The book adopts a step-by-step approach to perform meta-analyses and interpret the results. Stata codes for meta-analyses are also provided. All popularly used meta-analytic methods and models – such as the fixed effect model, random effects model, inverse variance heterogeneity model, and quality effect model – are used to find the confidence interval for the effect size measure of independent primary studies and the pooled study. In addition to the commonly used meta-analytic methods for various effect size measures, the book includes special topics such as meta-regression, dose-response meta-analysis, and publication bias. The main attraction for readers is the book’s simplicity and straightforwardness in conducting actual meta-analysis using MetaXL. Researchers would easily find everything on meta-analysis of any particular effect size in one specific chapter once they could determine the underlying effect measure. Readers will be able to see the results under different models and also will be able to select the correct model to obtain accurate results.
Estimation and Selection in High-Dimensional Genomic Studies
| Author | : Hisashi Noma |
| Publisher | : Springer |
| Total Pages | : 90 |
| Release | : 2020-04-23 |
| Genre | : Medical |
| ISBN | : 9784431555667 |
This book provides an overview of the statistical methods used in genome-wide screening of relevant genomic features or genes. Gene screening can facilitate deeper understanding of disease biology at the molecular level, possibly leading to discovery of new molecular targets for developing new treatments and developing diagnostic tests to predict patients’ prognosis or response to treatment. The most common approach to such gene screening studies is to apply multiple univariate analysis based on separate statistical tests for individual genes to test the null hypothesis of no association with clinical variables. This book first provides an overview of the state of the art of such multiple testing methodologies for gene screening, including frequentist multiple tests, empirical Bayes, and full-Bayes model-based methods for controlling the family-wise error rate or false discovery rate. Optimal discovery procedures and model-based variants are also discussed. Although great endeavor has been directed toward developing multiple testing methods, there are other, more relevant and effective analyses that should be given much attention in gene screening, including gene ranking, estimation of effect sizes, and classification accuracy based on selected genes. The core contents of this book provide a framework for integrated gene screening analysis based on hierarchical mixture modeling and empirical Bayes. Within this framework effective tools for multiple testing, ranking, estimation of effect size, and classification accuracy are derived. Methods for sample size determination for gene screening studies are also provided. With this content, the book is certain to expand the existing framework of statistical analysis based on multiple testing for gene screening to one based on estimation and selection.
Biostatistical Genetics and Genetic Epidemiology
| Author | : Robert C. Elston |
| Publisher | : John Wiley & Sons |
| Total Pages | : 860 |
| Release | : 2002-04-22 |
| Genre | : Medical |
| ISBN | : 9780471486312 |
"Human Genetics and Genetic Epidemiology" ist der 3. Band aus der sehr erfolgreichen Reihe 'Wiley Biostatistics Reference Series', die auf Artikeln der "Encyclopedia of Biostatistics" basiert. Dieser Band gibt einen topaktuellen und umfassenden Überblick über ein Forschungsgebiet, das insbesondere im Zuge des Human-Genomprojekts eine regelrechte Explosion an Forschungsaktivitäten erlebt hat. Enthalten sind komplett aktualisierte Artikel aus der "Encyclopedia of Biostatistics" sowie über 25% neue Artikel. Mit einem komplexen System an Querverweisen, die das Auffinden der gewünschten Information erheblich erleichtern. Eine interessante Lektüre für Genetiker, Epidemiologen, Biostatistiker und Forscher in diesen Bereichen.
Individual Participant Data Meta-Analysis
| Author | : Richard D. Riley |
| Publisher | : John Wiley & Sons |
| Total Pages | : 38 |
| Release | : 2021-06-08 |
| Genre | : Medical |
| ISBN | : 1119333725 |
Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points. Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data. Intended for a broad audience, the book will enable the reader to: Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review Recognise the scope, resources and challenges of IPD meta-analysis projects Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators Understand how to obtain, check, manage and harmonise IPD from multiple studies Examine risk of bias (quality) of IPD and minimise potential biases throughout the project Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research Critically appraise existing IPD meta-analysis projects Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models Detailed examples and case studies are provided throughout.
Analysis of Complex Disease Association Studies
| Author | : Eleftheria Zeggini |
| Publisher | : Academic Press |
| Total Pages | : 353 |
| Release | : 2010-11-17 |
| Genre | : Medical |
| ISBN | : 0123751438 |
According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests Extensive list of references including links to tutorial websites Case studies and Tips and Tricks
