Understanding clinical data analysis : learning statistical principles from published clinical research /

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Bibliographic Details
Author / Creator:Cleophas, Ton J. M., author.
Imprint:Switzerland : Springer, [2016]
©2017
Description:1 online resource (x, 234 pages) : illustrations (some color)
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11266312
Hidden Bibliographic Details
Other authors / contributors:Zwinderman, Aeilko H., author.
ISBN:9783319395869
3319395866
9783319395852
3319395858
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (SpringerLink, viewed August 29, 2016).
Summary:This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings. In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.
Other form:Print version: Cleophas, Ton J. M. Understanding clinical data analysis. Switzerland : Springer, [2016] 3319395858 9783319395852
Standard no.:10.1007/978-3-319-39586-9

MARC

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505 0 |a Randomness -- Randomized and Observational Research -- Randomized Clinical Trials, history, Designs -- Randomized Clinical Trials, Analysis Sets, Statistical Analysis, Reporting Issues -- Discrete Data Analysis, Failure Time Data Analysis -- Quantitative Data Analysis -- Subgroup Analysis -- Interim Analysis -- Multiplicity Analysis -- Medical Statistics : a Discipline at the Interface of Biology and Mathematics. 
504 |a Includes bibliographical references and index. 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed August 29, 2016). 
520 |a This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings. In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs. 
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