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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? Thebook will cover the WHY-SOs.
Table des matières
Preface.- Randomness.- Randomized and Observational Research.- Randomized Clinical Trials, 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.-Index.
A propos de l'auteur
The authors
are well-qualified in their field. Professor Zwinderman is past-president of
the International Society of Biostatistics (2012-2015), and Professor Cleophas
is past-president of the American
College of Angiology
(2000-2002). From their expertise they should be able to make adequate
selections of modern methods for clinical data analysis for the benefit of
physicians, students, and investigators. The authors have been working and
publishing together for 17 years, and their research can be characterized as a
continued effort to demonstrate that clinical data analysis is not mathematics
but rather a discipline at the interface of biology and mathematics.
The authors as professors and teachers in
statistics at universities in The Netherlands and France for the most part of
their lives, are convinced that the scientific method of statistical reasoning
and hypothesis testing is little used by physicians and other health workers,
and they hope that the current productionwill help them find the appropriate
ways for answering their scientific questions.
Three
textbooks complementary to the current production and written by the same
authors are Statistics applied to clinical studies 5th edition, 2012, Machine
learning in medicine a complete overview, 2015, SPSS for starters and 2nd
levelers, 2015, all of them edited by Springer Heidelberg Germany.
Résumé
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? Thebook will cover the
WHY-SOs.
