Understanding Clinical Data Analysis

Learning Statistical Principles from Published Clinical Research
1. Auflage 2017
ISBN: 978-3-319-39585-2
Verlag: Springer International Publishing

Buch, Englisch, 234 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 4912 g

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.

Zielgruppe

Graduate

Autoren/Hrsg.

Zwinderman, Aeilko H.

Cleophas, Ton J.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Krankenhausmanagement, Praxismanagement
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik

Inhaltsverzeichnis

Chapter 1

Randomness

Basis of All Scientific Methods



Chapter 2

Randomized and Observational Research

Writing Protocols, Making Study Data Files



Chapter 3

Randomized Clinical Trials, Designs

Questionable Use of Placebos and Lack of Placebos, Stepped Wedge and Adaptive Designs



Chapter 4

Randomized Clinical Trials, Analysis Sets, Statistical Analysis, Reporting Issues

Principal Features of Statistical Analyses, the Cochrane Risk-of-Bias-Tool



Chapter 5

Discrete Data Analysis, Failure Time Data Analysis

Better Assessments of Biological and Pharmaceutical Agents



Chapter 6

Quantitative Data Analysis

Modeling for False Positive Findings, Using Median Absolute Deviations



Chapter 7

Subgroup Analysis

European Medicines Agency's and American Food Drug Administration's Directives



Chapter 8

Interim Analysis

Alpha Spending Function Approach



Chapter 9

Multiplicity Analysis

Gate Keeping Strategies and Closure Principles



Chapter 10

Medical Statistics, a Discipline at the Interface of Biology and Mathematics

Equating Subjective Feelings with Probabilities, and Providing Quality Criteria for Diagnostic Tests

Über Autor(innen)

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 choose the best-help-there-is for making difficult issues understandable, that is real data examples from the global literature rather than hypothetical examples.

The authors have been working and publishing together for 18 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 philosophy, 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 production will help them find the appropriate ways for answering their scientific questions.

Four 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 2nd edition, 2015, Clinical Data Analysis on a Pocket Calculator 2nd edition, 2016, all of them edited by Springer Heidelberg Germany.