Authors:
- It shows, for the first time, that machine learning methodologies can be used for assessing efficacy data of controlled clinical trials
- It confirms, that machine learning methodologies provide better sensitivity of testing
- It confirms, that machine learning methodologies are more informative
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Table of contents (20 chapters)
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Front Matter
About this book
Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to even out multiple variables
Modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required
This is the first publication of clinical trials that have been systematically analyzed with machine learning. In addition, all of the machine learning analyses were tested against traditional analyses. Step by step statistics for self-assessments are included
The authors conclude, that machine learning is often more informative, and provides better sensitivities of testing than traditional analytic methods do
Authors and Affiliations
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Albert Schweitzer Hospital, Department Medicine, Sliedrecht, The Netherlands
Ton J. Cleophas
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Dept. Biostatistics and Epidemiology, Academic Medical Center, Amsterdam, The Netherlands
Aeilko H. Zwinderman
About the authors
Professor Zwinderman is one of the Principle Investigators of the Academic Medical Center Amsterdam, and his research is concerned with developing statistical methods for new research designs in biomedical science, particularly integrating omics data, like genomics, proteomics, metabolomics, and analysis tools based on parallel computing and the use of cluster computers and grid computing.
Professor Cleophas is a member of the Academic Committee of the European College of Pharmaceutical Medicine, that provides, on behalf of 22 European Universities, the Master-ship trainings "Pharmaceutical Medicine" and "Medicines Development".
From their expertise theyshould 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 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 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 concerned, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis, and they do hope that the current production "Regression Analysis for Starters and 2nd Levelers" will be a helpful companion for the purpose.
Five textbookscomplementary 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,
Modern Meta-analysis, 2017
Regression Analysis in Medical Research, 2018
all of them published by Springer
Bibliographic Information
Book Title: Efficacy Analysis in Clinical Trials an Update
Book Subtitle: Efficacy Analysis in an Era of Machine Learning
Authors: Ton J. Cleophas, Aeilko H. Zwinderman
DOI: https://doi.org/10.1007/978-3-030-19918-0
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-19917-3Published: 25 September 2019
Softcover ISBN: 978-3-030-19920-3Published: 25 September 2020
eBook ISBN: 978-3-030-19918-0Published: 03 September 2019
Edition Number: 1
Number of Pages: XI, 304
Number of Illustrations: 251 b/w illustrations, 44 illustrations in colour
Topics: Biomedicine general, Statistics for Life Sciences, Medicine, Health Sciences, Biostatistics