Articles liés à Applied Meta-Analysis with R and Stata
Synopsis
In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, this book shows how to implement statistical meta-analysis methods to real data using R and Stata.
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À propos de l?auteur
Ding-Geng (Din) Chen is a fellow of American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina-Chapel Hill, USA. Formerly, he was a Professor of Biostatistics at the University of Rochester, New York, USA, the Karl E. Peace Endowed Eminent Scholar Chair and professor in Biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University, USA, and a professor of statistics at South Dakota Stata University, USA. Dr. Chen's research interests include clinical trial biostatistical methodological development in Bayesian models, survival analysis, multi-level modelling and longitudinal data analysis, and statistical meta-analysis. He has published more than 200 refereed papers and co-authored/co-edited 30 book in statistics.
Karl E. Peace is the Georgia Cancer Coalition Distinguished Cancer Scholar, Founding Director of the Center for Biostatistics, Professor of Biostatistics, and Senior Research Scientist in the Jiann-Ping Hsu College of Public Health at Georgia Southern University (GSU). Dr. Peace has made pivotal contributions in the development and approval of drugs to treat numerous diseases and disorders. A fellow of the ASA, he has been a recipient of many honors, including the Drug Information Association Outstanding Service Award, the American Public Health Association Statistics Section Award, The First recipient of the President's Medal for outstanding contributions to GSU, and recognition by the Georgia and US Houses of Representatives, and the Virginia House of Delegates.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurChapman and Hall/CRC
- Date d'édition2021
- ISBN 10 0367183838
- ISBN 13 9780367183837
- ReliureRelié
- Langueanglais
- Numéro d'édition2
- Nombre de pages456
- Coordonnées du fabricantnon disponible
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Statistical Meta-Analysis using R and Stata (Chapman & Hall/CRC Biostatistics Series)
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Chapman and Hall/CRC 2021-03-11, 2021
ISBN 10 : 0367183838
ISBN 13 : 9780367183837
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Applied Meta-Analysis with R and Stata
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Applied Meta-Analysis with R and Stata
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Statistical Meta-Analysis using R and Stata
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Ding-Geng (Din) Chen is a fellow of American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina-Chapel Hill, USA. Formerly, he was a Professor of Biostatistics at the U. N° de réf. du vendeur 417369137
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Applied Meta-Analysis with R and Stata
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Taylor & Francis Group, 2021
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ISBN 13 : 9780367183837
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Statistical Meta-Analysis using R and Stata
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ISBN 13 : 9780367183837
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Review of the First Edition:The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis.A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs.-Journal of Applied StatisticsStatistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions.What's New in the Second Edition:Adds Stata programs along with the R programs for meta-analysisUpdates all the statistical meta-analyses with R/Stata programsCovers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SSAdds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MASuitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry. 424 pp. Englisch. N° de réf. du vendeur 9780367183837
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Applied Meta-Analysis with R and Stata
Edité par
Chapman And Hall/CRC, 2021
ISBN 10 : 0367183838
ISBN 13 : 9780367183837
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Review of the First Edition:The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis.A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs.-Journal of Applied StatisticsStatistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions.What's New in the Second Edition:Adds Stata programs along with the R programs for meta-analysisUpdates all the statistical meta-analyses with R/Stata programsCovers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SSAdds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MASuitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry. N° de réf. du vendeur 9780367183837
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Applied Meta-Analysis with R and Stata (Chapman & Hall/CRC Biostatistics Series)
Edité par
Chapman and Hall/CRC, 2021
ISBN 10 : 0367183838
ISBN 13 : 9780367183837
Neuf
Couverture rigide
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Applied Meta-Analysis with R and Stata (Chapman & Hall/CRC Biostatistics Series)
Edité par
Chapman and Hall/CRC, 2021
ISBN 10 : 0367183838
ISBN 13 : 9780367183837
Neuf
Couverture rigide
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