Advertisement

© 2019

Efficacy Analysis in Clinical Trials an Update

Efficacy Analysis in an Era of Machine Learning

Textbook

Table of contents

  1. Front Matter
    Pages i-xi
  2. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 1-35
  3. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 37-53
  4. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 55-61
  5. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 63-73
  6. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 75-85
  7. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 87-94
  8. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 95-105
  9. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 107-118
  10. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 119-135
  11. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 137-146
  12. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 147-171
  13. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 173-184
  14. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 185-194
  15. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 195-210
  16. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 211-221
  17. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 223-236
  18. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 237-251
  19. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 253-267
  20. Ton J. Cleophas, Aeilko H. Zwinderman
    Pages 269-278

About this book

Introduction

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.

Keywords

Clinical trials Traditional efficacy analysis Machine learning for efficacy analysis Data mining Big data validation

Authors and affiliations

  1. 1.Albert Schweitzer HospitalDepartment MedicineSliedrechtThe Netherlands
  2. 2.Dept. Biostatistics and EpidemiologyAcademic Medical CenterAmsterdamThe Netherlands

About the authors

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). 

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