Statistical Modeling for Biomedical Researchers

Statistical Modeling for Biomedical Researchers
Title Statistical Modeling for Biomedical Researchers PDF eBook
Author William D. Dupont
Publisher Cambridge University Press
Pages 543
Release 2009-02-12
Genre Medical
ISBN 0521849527

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A second edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.

Statistical Modeling for Biomedical Researchers

Statistical Modeling for Biomedical Researchers
Title Statistical Modeling for Biomedical Researchers PDF eBook
Author William D. Dupont
Publisher Cambridge University Press
Pages 543
Release 2009-02-12
Genre Medical
ISBN 1139643819

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The second edition of this standard text guides biomedical researchers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is again used to perform the analyses, this time employing the much improved version 10 with its intuitive point and click as well as character-based commands. Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available at http://biostat.mc.vanderbilt.edu/dupontwd/wddtext/.

Statistical Modeling in Biomedical Research

Statistical Modeling in Biomedical Research
Title Statistical Modeling in Biomedical Research PDF eBook
Author Yichuan Zhao
Publisher Springer Nature
Pages 495
Release 2020-03-19
Genre Medical
ISBN 3030334163

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This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in: Next generation sequence data analysis Deep learning, precision medicine, and their applications Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its applications.

Statistical Methods for the Analysis of Biomedical Data

Statistical Methods for the Analysis of Biomedical Data
Title Statistical Methods for the Analysis of Biomedical Data PDF eBook
Author Robert F. Woolson
Publisher John Wiley & Sons
Pages 714
Release 2011-01-25
Genre Medical
ISBN 111803130X

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Dieser Band behandelt eine Reihe statistischer Themen, die bei der Analyse biologischer und medizinischer Daten allgemein Anwendung finden. Diese 2. Auflage wurde komplett überarbeitet, aktualisiert und erweitert. Einige Kapitel sind neu hinzugekommen, u.a. zur multiplen linearen Regression in der biomedizinischen Forschung. Der Stoff ist so gegliedert, dass der Leser den Text unabhängig von der jeweiligen statistischen Methode leicht nach Problemstellungen durchsuchen kann. Mit zahlreichen durchgearbeiteten Beispielen, die detaillierte Lösungsangaben zu Problemen aus der Praxis liefern.

Statistical Modeling for Biomedical Researchers

Statistical Modeling for Biomedical Researchers
Title Statistical Modeling for Biomedical Researchers PDF eBook
Author
Publisher
Pages 522
Release 2009
Genre Medicine
ISBN 9780511480102

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New edition of the easy-to-use standard text guiding biomedical researchers in the use of advanced statistical methods.

Essential Statistical Methods for Medical Statistics

Essential Statistical Methods for Medical Statistics
Title Essential Statistical Methods for Medical Statistics PDF eBook
Author J. Philip Miller
Publisher Elsevier
Pages 363
Release 2010-11-08
Genre Mathematics
ISBN 0444537384

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Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas. - Contributors are internationally renowned experts in their respective areas - Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research - Methods for assessing Biomarkers, analysis of competing risks - Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs - Structural equations modelling and longitudinal data analysis

Regression Modeling Strategies

Regression Modeling Strategies
Title Regression Modeling Strategies PDF eBook
Author Frank E. Harrell
Publisher Springer Science & Business Media
Pages 583
Release 2013-03-09
Genre Mathematics
ISBN 147573462X

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Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".