Efficacy Analysis in Clinical Trials an Update Efficacy Analysis in
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 l
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cacy Analysis in Clinical Trials an Update Efficacy Analysis in an Era of Machine Learning
Efficacy Analysis in Clinical Trials an Update
Ton J. Cleophas • Aeilko H. Zwinderman
Efficacy Analysis in Clinical Trials an Update Efficacy Analysis in an Era of Machine Learning
Ton J. Cleophas Albert Schweitzer Hospital Department Medicine Sliedrecht, The Netherlands
Aeilko H. Zwinderman Dept. Biostatistics and Epidemiology Academic Medical Center Amsterdam, The Netherlands
Additional material to this book can be downloaded from http://extras.springer.com. ISBN 978-3-030-19917-3 ISBN 978-3-030-19918-0 https://doi.org/10.1007/978-3-030-19918-0
(eBook)
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Preface
The title Machine Learning in Medicine: A Complete Overview from the same authors was published by Springer in 2014. It showed that machine learning is helpful for the analysis of observational clinical research and surveys. To date machine learning has been rarely applied for the analysis of clinical trials. Clinical trials have been developed for efficacy assessment of new medical treatments. They are, traditionally, assessed with continuous variables, and analyzed with t-statistic or analysis of variance. These tests are unable to handle many variables but this is no drawback, because multiple variables tend to even out by the randomization process, and are not further taken into account in the analysis. In contrast, modern medical computer files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. Fortunately, with the advent of the computer, a novel type of data analysis has devel
