Main description:

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

Contents:

Preface

Contents

Chapter 1

Traditional and Machine-Learning Methods for Efficacy Analysis

1. Introduction

2. The Principle of Testing Statistical Significance

3. The T-Value = a Standardized Mean Result of a Study

4. Unpaired T-Test

5. Null-Hypothesis Testing of Three or More Unpaired Samples

6. Three Methods to Test Statistically a Paired Sample

7. Null-Hypothesis Testing of Three or More Paired Samples

8. Null Hypothesis Testing with Complex Data

9. Paired Data with a Negative Correlation

10. Rank Testing

11. Rank Testing for Three or More Samples

12. Regression Analysis in the Efficacy Analysis of Clinical Trials

13. Predictors in Clinical Trials

14. Discrete and Discretized Data for Efficacy Analysis

15. Summary of Traditional Methods for Efficacy Analysis Applied in this Edition

16. Summary of Machine Learning Methods for Efficacy Analysis

17. Discussion

18. References

Chapter 2

Optimal-Scaling for Efficacy Analysis

1. Introduction

2. Example

3. Traditional Efficacy analysis

4. Optimal Scaling for Efficacy Analysis

5. Discussion

6. References

Chapter 3

Ratio-Statistic for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Ratio-Statistic for Efficacy Analysis

5. Discussion

6. References

Chapter 4

Complex-Samples for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Complex-Samples for Efficacy Analysis

5. Discussion

6. References

Chapter 5

Bayesian-Networks for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Bayesian-Network for Efficacy Analysis

5. Discussion

6. References

Chapter 6

Evolutionary-Operations for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Evolutionary-Operations for Efficacy Analysis

5. Discussion

6. References

Chapter 7

Automatic-Newton-Modeling for Efficacy Analysis

1. Introduction

2. Traditional Efficacy Analysis

Dose-Effectiveness Study

Time-Concentration Study

3. Automatic-Newton-Modeling for Efficacy Analysis

Dose-Effectiveness Study

Time-Concentration Study

4. Discussion

5. References

Chapter 8

High-Risk-Bins for Efficacy Analysis

1. Introduction

2. Traditional Efficacy Analysis

The Fruit table

The Snacks table

The Fastfood table

The Physicalactivities table

3. High-Risk-Bins for Efficacy Analysis

4. Discussion

5. References

Chapter 9

Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis

1. Introduction

2. Traditional Efficacy Analysis

Example 1

Example 2

3. Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis

Example 1

Example 2

4. Discussion

5. References

Chapter 10

Cluster-Analysis for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Cluster Analysis for Efficacy Analysis

1. Hierarchical cluster analysis

2. K-means cluster analysis

3. Density-based cluster analysis

5. Discussion

6. References

Chapter 11

Multidimensional-Scaling for Efficacy Analysis

1. Introduction

2. Traditional Efficacy Analysis

3. Multidimensional Scaling for Efficacy Analysis

1. Proximity Scaling

2. Preference Scaling

4. Discussion

5. References

Chapter 12

Binary Decision-Trees for Efficacy Analysis

1. Introduction

2. Data Example with Binary Outcome

3. Traditional Efficacy Analysis

4. Decision-Trees for Efficacy analysis

5. Discussion

6. References

Chapter 13

Continuous Decision-Trees for Efficacy Analysis

1. Introduction

2. Data Example with a Continuous Outcome

3. Traditional Efficacy Outcome

4. Decision-Trees for Efficacy Analysis

5. Discussion

6. References

Chapter 14

Automatic-Data-Mining for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Automatic-Data-Mining for Efficacy Analysis

1. Step 1 open SPSS modeler

2. Step 2 the distribution node

3. Step 3 the audit node

4. Step 4 the plot node

5. Step 5. the web node

6. Step 6 the type and c5.0 nodes

7. Step 7 the output node

5. Discussion

6. References

Chapter 15

Support-Vector-Machines for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy analysis

4. Support-Vector-Machines for Efficacy Analysis

1. File reader node

2. The nodes x-partitioner, svm learner, x-aggregator

3. Error rates

4. Prediction table

5. Discussion

6. References

Chapter 16

Neural-Networks for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Neural-Networks Efficacy Analysis

5. Discussion

6. References

Chapter 17

Ensembled-Accuracies for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Ensembled-Accuracies for Efficacy Analysis

1. Step 1 open SPSS modeler

2. Step 2 the statistics file node

3. Step 3 the type node

4. Step 4 the auto classifier node

5. step 5 the expert tab

6. step 6 the settings tab

7. step7 the analysis node

5. Discussion

6. References

Chapter 18

Ensembled-Correlations for Efficacy Analysis

1. Introduction

2. Example

3. Traditional Efficacy Analysis

4. Ensembled-Correlations for Efficacy Analysis

1. Step 1 open SPSS modeler

2. Step 2 the statistics file node

3. Step 3 the type node

4. Step 4 the auto numeric node

5. Step 5 the expert node step

6. Step 6 the settings tab

7. Step 7 the analysis node

5. Discussion

6. References

Chapter 19

Gamma-Distributions for Efficacy Analysis

1. Introduction

2. Data Example

3. Traditional Efficacy Analysis

4. Gamma-Distributions for Efficacy Analysis

5. Discussion

6. References

Chapter 20

Validation with Big Data, a Big Issue

1. Introduction

2. Semantics of the Term Validation

3. Clinical Trial Validation

4. Diagnostic Test Validation

5. Big Data Validation

6. Big Data Jargon

7. Discussion

8. References

Index