Modern Survival Analysis in Clinical Research: Cox Regressions versus Accelerated Failure Time Models

ISBN-13: 9783031316319 / Angielski

Ton J. Cleophas; Aeilko H. Zwinderman

Opis

An important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss Machines) in its SPSS statistical software update of 2023. Unlike the traditional Cox regressions that work with hazards, which are the ratio of deaths and non-deaths in a sample, it works with risk of death, which is the proportion of deaths in the same sample. The latter approach may provide better sensitivity of testing, but has been seldom applied, because with computers risks are tricky and hazards because they are odds are fine. This was underscored in 1997 by Keiding and colleague statisticians from Copenhagen University who showed better-sensitive goodness of fit and null-hypothesis tests with AFT than with Cox survival tests.So far, a controlled study of a representative sample of clinical Kaplan Meier assessments, where the sensitivity of Cox regression is systematically tested against that of AFT modeling, has not been accomplished. This edition is the first textbook and tutorial of AFT modeling both for medical and healthcare students and for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional Cox regressions. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment.We should add that 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). From their expertise they should be able to make adequate selections of modern data analysis methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 25 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.

Spis treści

Preface

Chapter 1: Regression Analysis

1.1 Introduction

1.2 History

1.3 Methodology of Regression Analysis

1.3.1 Linear Regression

1.3.2 Logistic Regression

1.3.3 Cox Regression

1.4 Conclusion

1.5 References

Chapter 2: Cox Regressions

2.1 Introduction

2.2 History of Cox Regressions

2.3 Principles of Cox Regressions

2.4 Conclusion

2.5 References

Chapter 3: Accelerated Failure Time Models

3.1 Introduction

3.2 History of Failure Time Models

3.3 Methodologies of Failure Time Models

3.4 Graphs of Successfyk Functions to Analyze Accelerated Failure Time Models

3.5 Conclusion

3.6 References

Chapter 4: Simple Dataset with Event as Outcome and Treatment as Predictor

4.1 Introduction

4.2 Data Example

4.3 Data Analysis Using SPSS Statistical Software Version 29

4.4 Cox Regression

4.5 Accelerated Failure Time Model with Weibull Distribution

4.6 Accelerated Failure Time Model with Exponential Distribution

4.7 Accelerated Failure Time Model with Log Normal Distribution

4.8 Accelerated Failure Time Model with Log Logistics Distribution

4.9 Conclusion

4.10 References

Chapter 5: Simple Dataset with Death as Outcome and Treatment Modality, Cholesterol, and Age as Predictors

5.1 Introduction

5.2 Data Example

5.3 Data Analysis Using SPSS Statistical Software Version 29

5.4 Three Predictors Cox Regression

5.5 Accelerated Failure Time Model with Weibull Distribution

5.6 Accelerated Failure Time Model with Exponential Distribution

5.7 Accelerated Failure Time Model with Log Normal Distribution

5.8 Accelerated Failure Time Model with Log Logistics Distribution

5.9 Conclusion

5.10 References

Chapter 6: Glioma Brain Cancer

6.1 Introduction

6.2 Data Example

6.3 Data Analysis Using SPSS Statistical Software Version 29

6.4 Cox Regression

6.5 Accelerated Failure Time Model with Weibull Distribution

6.6 Accelerated Failure Time Model with Exponential Distribution

6.7 Accelerated Failure Time Model with Log Normal Distribution

6.8 Accelerated Failure Time Model with Log Logistics Distribution

6.9 Conclusion

6.10 References

Chapter 7: Linoleic Acid for Colonic Carcinoma

7.1 Introduction

7.2 Data Example

7.3 Data Analysis Using SPSS Statistical Software Version 29

7.4 Cox Regression

7.5 Accelerated Failure Time Model with Weibull Distribution

7.6 Accelerated Failure Time Model with Exponential Distribution

7.7 Accelerated Failure Time Model with Log Normal Distribution

7.8 Accelerated Failure Time Model with Log Logistics Distribution

7.9 Conclusion

7.10 References

Chapter 8: The Effect on Survival of Maintained Chemotherapy with Acute Myelogenous Leucemia

8.1 Introduction

8.2 Data Example

8.3 Data Analysis Using SPSS Statistical Software Version 29

8.4 Cox Regression

8.5 Accelerated Failure Time Model with Weibull Distribution

8.6 Accelerated Failure Time Model with Exponential Distribution

8.7 Accelerated Failure Time Model with Log Normal Distribution

8.8 Accelerated Failure Time Model with Log Logistics Distribution

8.9 Conclusion

8.10 References

Chapter 9: Eighty Four Month Parallel Group Mortality Study

9.1 Introduction

9.2 Data Example

9.3 Data Analysis Using SPSS Statistical Software Version 29

9.4 Cox Regression

9.5 Accelerated Failure Time Model with Weibull Distribution

9.6 Accelerated Failure Time Model with Exponential Distribution

9.7 Accelerated Failure Time Model with Log Normal Distribution

9.8 Accelerated Failure Time Model with Log Logistics Distribution

9.9 Conclusion

9.10 References

Chapter 10: The Effect on Survival from Stages 1 and 2 Histiocytic Lymphoma

10.1 Introduction

10.2 Data Example

10.3 Data Analysis Using SPSS Statistical Software Version 29

10.4 Cox Regression

10.5 Accelerated Failure Time Model with Weibull Distribution

10.6 Accelerated Failure Time Model with Exponential Distribution

10.7 Accelerated Failure Time Model with Log Normal Distribution

10.8 Accelerated Failure Time Model with Log Logistics Distribution

10.9 Conclusion

10.10 References

Chapter 11: Survival of 64 Lymphoma Patients with or without B Symptoms

11.1 Introduction

11.2 Data Example

11.3 Data Analysis Using SPSS Statistical Software Version 29

11.4 Cox Regression

11.5 Accelerated Failure Time Model with Weibull Distribution

11.6 Accelerated Failure Time Model with Exponential Distribution

11.7 Accelerated Failure Time Model with Log Normal Distribution

11.8 Accelerated Failure Time Model with Log Logistics Distribution

11.9 Conclusion

11.10 References

Chapter 12: Effect on Time-to-Event of Group Membership

12.1 Introduction

12.2 Data Example

12.3 Data Analysis Using SPSS Statistical Software Version 29

12.4 Cox Regression

12.5 Accelerated Failure Time Model with Weibull Distribution

12.6 Accelerated Failure Time Model with Exponential Distribution

12.7 Accelerated Failure Time Model with Log Normal Distribution

12.8 Accelerated Failure Time Model with Log Logistics Distribution

12.9 Conclusion

12.10 References

Chapter 13: The Effect on Survival of Group Membership

13.1 Introduction

13.2 Data Example

13.3 Data Analysis Using SPSS Statistical Software Version 29

13.4 Cox Regression

13.5 Accelerated Failure Time Model with Weibull Distribution

13.6 Accelerated Failure Time Model with Exponential Distribution

13.7 Accelerated Failure Time Model with Log Normal Distribution

13.8 Accelerated Failure Time Model with Log Logistics Distribution

13.9 Conclusion

13.10 References

Chapter 14: Deaths from Carcinoma after Exposure to Carcinogens in Rats

14.1 Introduction

14.2 Data Example

14.3 Data Analysis Using SPSS Statistical Software Version 29

14.4 Cox Regression

14.5 Accelerated Failure Time Model with Weibull Distribution

14.6 Accelerated Failure Time Model with Exponential Distribution

14.7 Accelerated Failure Time Model with Log Normal Distribution

14.8 Accelerated Failure Time Model with Log Logistics Distribution

14.9 Conclusion

14.10 References

Chapter 15: Effect of Group Membership on Survival

15.1 Introduction

15.2 Data Example

15.3 Data Analysis Using SPSS Statistical Software Version 29

15.4 Cox Regression

15.5 Accelerated Failure Time Model with Weibull Distribution

15.6 Accelerated Failure Time Model with Exponential Distribution

15.7 Accelerated Failure Time Model with Log Normal Distribution

15.8 Accelerated Failure Time Model with Log Logistics Distribution

15.9 Conclusion

15.10 References

Chapter 16: Multiple Variables Regression Study of 2421 Stroke Patients Assessed for Time to Second Stroke

16.1 Introduction and Sata Example

16.2 Data Analysis Using SPSS Statistical Software Version 29

16.3 Cox Regression

16.4 Accelerated Failure Time Model with Weibull Distribution

16.5 Accelerated Failure Time Model with Exponential Distribution

16.6 Accelerated Failure Time Model with Log Normal Distribution

16.7 Accelerated Failure Time Model with Log Logistics Distribution

16.8 Conclusion

16.9 References

Chapter 17: Hypothesized 55 Patient Study of Effect of Treatment Modality on Survival

17.1 Introduction

17.2 Data Example

17.3 Data Analysis Using SPSS Statistical Software Version 29

17.4 Cox Regression

17.5 Accelerated Failure Time Model with Weibull Distribution

17.6 Accelerated Failure Time Model with Exponential Distribution

17.7 Accelerated Failure Time Model with Log Normal Distribution

17.8 Accelerated Failure Time Model with Log Logistics Distribution

17.9 Conclusion

17.10 References

Chapter 18: One Year Follow-up Study with Many Censored Patients

18.1 Introduction

18.2 Data Example

18.3 Data Analysis Using SPSS Statistical Software Version 29

18.4 Cox Regression

18.5 Accelerated Failure Time Model with Weibull Distribution

18.6 Accelerated Failure Time Model with Exponential Distribution

18.7 Accelerated Failure Time Model with Log Normal Distribution

18.8 Accelerated Failure Time Model with Log Logistics Distribution

18.9 Conclusion

18.10 References

Chapter 19: Alcohol Relapse after Detox Program Treated with or without Personal Coach

19.1 Introduction

19.2 Data Example

19.3 Data Analysis Using SPSS Statistical Software Version 29

19.4 Cox Regression

19.5 Accelerated Failure Time Model with Weibull Distribution

19.6 Accelerated Failure Time Model with Exponential Distribution

19.7 Accelerated Failure Time Model with Log Normal Distribution

19.8 Accelerated Failure Time Model with Log Logistics Distribution

19.9. Conclusion

19.10 References

Chapter 20: Alcohol Relapse after Detox Program with 3 Predictors

20.1 Introduction

20.2 Data Example

20.3 Data Analysis Using SPSS Statistical Software Version 29

20.4 Cox Regression

20.5 Accelerated Failure Time Model with Weibull Distribution

20.6 Accelerated Failure Time Model with Exponential Distribution

20.7 Accelerated Failure Time Model with Log Normal Distribution

20.8 Accelerated Failure Time Model with Log Logistics Distribution

20.9 Conclusion

20.10 References

Chapter 21: Ayurvedic Therapy for Human Immunodeficiency Virus

21.1 Introduction

21.2 Data Example

21.3 Data Analysis Using SPSS Statistical Software Version 29

21.4 Cox Regression

21.5 Accelerated Failure Time Model with Weibull Distribution

21.6 Accelerated Failure Time Model with Exponential Distribution

21.7 Accelerated Failure Time Model with Log Normal Distribution

21.8 Accelerated Failure Time Model with Log Logistics Distribution

21.9 Conclusion

21.10 References

Chapter 22: Time to Event other Than Cox

22.1 Introduction

22.2 Cox with Time Dependent Predictors

22.3 Segmented Cox

22.4 Interval Censored Regressions

22.5 Autocorrelations

22.6 Polynomial Regressions

22.7 Conclusion

22.8 References

Chapter 23: Abstracts of the Chapters 1 to 22

References

O autorze

Ton J. Cleophas is internist-clinical pharmacologist at the Department of Medicine Albert Schweitzer Hospital Dordrecht the Netherlands. He is also professor of Statistics and member of the Scientific Committee of the European College of Pharmaceutical Medicine Lyon France. He is particularly interested in machine learning methodologies and published many complete-overview-textbooks of the subject.

Aeilko H. Zwinderman is professor of Statistics and Chair of the Department of Biostatistics and Epidemiology at the University of Amsterdam the Netherlands. His current work focuses on development and validation of multivariable models, particularly in genetic research, and he is a major developer of penalized canonical analysis.