Overview
Provides novel and better sensitive methodologies for assessing and testing adverse effects based on machine learning clusters and networks, and multivariate analyses
Another important novelty with safety data analysis is new insights into hypothesis testing, favoring the alternative hypotheses over the null hypotheses
The authors give a systematic presentation of so far poorly recognized dependent adverse effects, and give special methods for detecting and managing them
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Table of contents (16 chapters)
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Front Matter
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The Analysis of Independent Adverse Effects
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Front Matter
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The Analysis of Dependent Adverse Effects
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Front Matter
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Back Matter
Keywords
About this book
An effective approach is to present summaries of the prevalence of adverse effects and their 95% confidence intervals. In order to estimate the probability that the differences between treatment and control group occurred merely by chance, a statistical test can be performed. In the past few years, this pretty crude method has been supplemented and sometimes, replaced with more sophisticated and better sensitive methodologies, based on machine learning clusters and networks, and multivariate analyses. As a result, it is time that an updated version of safety data analysis was published.
The issue of dependency also needs to be addressed. Adverse effects may be either dependent or independent of the main outcome. For example, an adverse effect of alpha blockers is dizziness and this occurs independently of the main outcome "alleviation of Raynaud 's phenomenon". In contrast, the adverse effect "increased calorie intake" occurs with "increased exercise", and this adverse effect is very dependent on the main outcome "weight loss". Random heterogeneities, outliers, confounders, interaction factors are common in clinical trials, and all of them can be considered as kinds of adverse effects of the dependent type. Random regressions and analyses of variance, high dimensional clusterings, partial correlations, structural equations models, Bayesian methods are helpful for their analysis.
The current edition was written for non-mathematicians, particularly medical and health professionals and students. It provides examples of modern analytic methods so far largely unused in safety analysis. All of the 14 chapters have two core characteristics, First, they are intended for current usage, and they are particularly concerned with that usage. Second, they try and tell what readers need to know in order to understand and apply the methods. For that purpose, step by step analyses of both hypothesized and real data examples are provided.Reviews
“The analysis of safety data in drug trials, or in other drug studies, is an important topic. … it is doubtful that this would make it a real book about safety analysis.” (ISCB News, Vol. 68, 2019)
Authors and Affiliations
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 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 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 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,
Modern Meta-analysis, 2017
Regression Analysis in Medical Research, 2018
all of them published by Springer
Bibliographic Information
Book Title: Analysis of Safety Data of Drug Trials
Book Subtitle: An Update
Authors: Ton J. Cleophas, Aeilko H. Zwinderman
DOI: https://doi.org/10.1007/978-3-030-05804-3
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-05803-6Published: 08 March 2019
eBook ISBN: 978-3-030-05804-3Published: 26 February 2019
Edition Number: 1
Number of Pages: XI, 217
Number of Illustrations: 163 b/w illustrations, 28 illustrations in colour
Topics: Biomedicine general, Statistics and Computing/Statistics Programs