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E-raamat: Data Analysis with Competing Risks and Intermediate States

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(Academic Medical Center and Public Health Service of Amsterdam, The Netherlands)
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Data Analysis with Competing Risks and Intermediate StatesSuurem pilt
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Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.

After introducing example studies from the biomedical and epidemiological fields, the book formally defines the concepts that play a role in analyses with competing risks and intermediate states. It addresses nonparametric estimation of the relevant quantities. The book then shows how to use a stacked data set that offers great flexibility in the modeling of covariable effects on the transition rates between states. It also describes three ways to quantify effects on the cumulative scale.

Each chapter includes standard exercises that reflect on the concepts presented, a section on software that explains options in SAS and Stata and the functionality in the R program, and computer practicals that allow readers to practice with the techniques using an existing data set of bone marrow transplant patients. The books website provides the R code for the computer practicals along with other material.

For researchers with some experience in the analysis of standard time-to-event data, this practical and thorough treatment extends their knowledge and skills to the competing risks and multi-state settings. Researchers from other fields can also easily translate individuals and diseases to units and phenomena from their own areas.

Arvustused

" a useful read for anyone wanting to apply competing risks or multi-state methods. The examples used throughout the book make the methods clinically meaningful for anyone wanting to simply grasp the concepts behind the methods, and the mathematical theory is rigorously described for those wanting a more in-depth understanding. The book is also supported by a website (http://www.competingrisks.org), which holds additional tips and R code to supplement the exercises at the end of each of the five chapters." Journal of Biopharmaceutical Statistics, 2015

"This book is excellent for applied statisticians working with time-to-event data." James J. Dignam, Department of Public Health Sciences, The University of Chicago

"An accessible introduction to the theory of competing risks and multistate models." Sandra Eloranta, Karolinska Institutet

"This book is about the particular context of competing risks and intermediate states. These risks or states have often been ignored in survival analysis but the situation is changing rapidly. The book is well written. Basic concepts of survival analysis are recalled and the reader is brought to the most complex concepts. Thus the book can be read both by beginners or experts in survival analysis. The reader can easily skip chapters that are not relevant according to his expertise and usefulness."

International Society for Clinical Biostatistics I thoroughly recommend the book and am sure that reading it will prompt many young students and researchers to further pursue such models and their applications, possibly embarking on a career in biomedical research. Carl M. OBrien, Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, UK

" a useful read for anyone wanting to apply competing risks or multi-state methods. The examples used throughout the book make the methods clinically meaningful for anyone wanting to simply grasp the concepts behind the methods, and the mathematical theory is rigorously described for those wanting a more in-depth understanding. The book is also supported by a website (http://www.competingrisks.org), which holds additional tips and R code to supplement the exercises at the end of each of the five chapters." Journal of Biopharmaceutical Statistics, 2015

"This book is excellent for applied statisticians working with time-to-event data." James J. Dignam, Department of Public Health Sciences, The University of Chicago

"An accessible introduction to the theory of competing risks and multistate models." Sandra Eloranta, Karolinska Institutet

"This book is about the particular context of competing risks and intermediate states. These risks or states have often been ignored in survival analysis but the situation is changing rapidly. The book is well written. Basic concepts of survival analysis are recalled and the reader is brought to the most complex concepts. Thus the book can be read both by beginners or experts in survival analysis. The reader can easily skip chapters that are not relevant according to his expertise and usefulness."

International Society for Clinical Biostatistics

Preface xiii
Acknowledgements xvii
About the Author xix
1 Basic Concepts 1(58)
1.1 Introduction
1(1)
1.2 Examples
2(5)
1.2.1 Infection during a hospital stay
2(1)
1.2.2 HIV infection
3(3)
1.2.3 Bone marrow transplantation
6(1)
1.3 Data structure
7(6)
1.3.1 Time scales
7(1)
1.3.2 Right censored data
8(2)
1.3.3 Left truncated data
10(3)
1.4 On rates and risks
13(2)
1.5 Non-informative observation schemes?
15(8)
1.5.1 Some possible solutions
20(3)
1.6 The examples revisited
23(5)
1.6.1 Infection during a hospital stay
23(1)
1.6.2 HIV infection
24(4)
1.6.3 Bone marrow transplantation
28(1)
1.7 Notation
28(2)
1.8 Basic techniques from survival analysis
30(13)
1.8.1 Main concepts and theoretical relations
30(2)
1.8.2 The Kaplan-Meier product-limit estimator
32(4)
1.8.2.1 Confidence intervals
34(2)
1.8.3 Nonparametric group comparisons
36(1)
1.8.4 Cox proportional hazards model
37(3)
1.8.5 Counting process format
40(3)
1.9 Summary and preview
43(1)
1.10 Exercises
44(6)
1.11 R code for classical survival analysis
50(6)
1.11.1 The aidssi data set
50(1)
1.11.2 Define time and status information
51(1)
1.11.3 Perform calculations
51(1)
1.11.4 Summary of outcome
52(3)
1.11.5 Log-rank test
55(1)
1.12 Computer practicals
56(3)
2 Competing Risks; Nonparametric Estimation 59(46)
2.1 Introduction
59(1)
2.2 Theoretical relations
60(3)
2.2.1 The multi-state approach; cause-specific hazards
60(2)
2.2.2 The subdistribution approach
62(1)
2.3 Estimation based on cause-specific hazard
63(5)
2.4 Estimation: the subdistribution approach
68(11)
2.4.1 Estimation with complete follow-up
70(1)
2.4.2 A special choice for Γ and φ
71(2)
2.4.3 The ECDF and PL forms
73(3)
2.4.3.1 The ECDF form
74(1)
2.4.3.2 The PL form
74(2)
2.4.4 Interpretation of the weighted estimators
76(3)
2.5 Standard errors and confidence intervals
79(4)
2.6 Log-rank tests and other subgroup comparisons
83(2)
2.7 Summary; three principles of interpretability
85(3)
2.8 Exercises
88(4)
2.9 Software
92(9)
2.9.1 Nonparametric estimation of Fk
92(8)
2.9.1.1 The Aalen-Johansen form
92(4)
2.9.1.2 The weighted product-limit form
96(4)
2.9.2 Log-rank tests
100(1)
2.10 Computer practicals
101(4)
3 Intermediate Events; Nonparametric Estimation 105(38)
3.1 Introduction; multi-state models
105(2)
3.2 Main concepts and theoretical relations
107(4)
3.2.1 Basic framework and definitions
107(4)
3.3 Estimation
111(4)
3.3.1 Data representation
111(1)
3.3.2 Nelson-Aalen and Aalen-Johansen estimator
111(4)
3.4 Example: HIV, SI, AIDS and death
115(10)
3.4.1 The data
116(1)
3.4.2 Analyses
117(8)
3.5 Summary; some alternative approaches
125(1)
3.6 Exercises
126(1)
3.7 Software
127(14)
3.7.1 The etm package
130(4)
3.7.2 The msSury package
134(4)
3.7.3 The mstate package
138(3)
3.8 Computer practicals
141(2)
4 Regression; Cause-Specific/Transition Hazard 143(40)
4.1 Introduction
143(1)
4.2 Regression on cause-specific hazard: basic structure
144(2)
4.3 Combined analysis and type-specific covariables
146(7)
4.3.1 Same results in one analysis
147(2)
4.3.2 Type-specific covariables
149(2)
4.3.3 Effects equal over causes
151(1)
4.3.4 Proportional baseline hazards
152(1)
4.4 Why does the stacked approach work?
153(3)
4.4.1 Cause as stratum variable
153(2)
4.4.2 Effects equal over causes
155(1)
4.4.3 Proportional baseline hazards
155(1)
4.5 Multi-state regression models for transition hazards
156(11)
4.5.1 Combined analyses: assume effects to be equal
159(1)
4.5.2 Proportional baseline hazards
160(4)
4.5.3 Dual role of intermediate states
164(1)
4.5.4 Beyond the Markov model: effect of transition time
165(2)
4.5.5 Standard error
167(1)
4.6 Example: causes of death in HIV infected individuals
167(9)
4.6.1 Analysis using well-defined contrasts
172(4)
4.7 Summary
176(1)
4.8 Exercises
177(2)
4.9 Software
179(1)
4.10 Computer practicals
180(3)
5 Regression; Translation to Cumulative Scale 183(30)
5.1 Introduction
183(1)
5.2 From cause-specific/transition hazard to probability
184(4)
5.2.1 Competing risks
184(3)
5.2.2 Multi-state models
187(1)
5.3 Regression on subdistribution hazard
188(11)
5.3.1 Choice of weight function
191(1)
5.3.2 Estimation of standard error
192(1)
5.3.3 Time-varying covariables
192(3)
5.3.4 Examples
195(4)
5.4 Multinomial regression
199(1)
5.5 Summary
200(2)
5.6 Exercises
202(1)
5.7 Software
202(7)
5.7.1 From cause-specific/transition hazard to probability
202(4)
5.7.2 Regression on subdistribution hazard
206(2)
5.7.3 Proportional odds model
208(1)
5.8 Computer practicals
209(4)
5.8.1 Multi-state analysis
209(4)
6 Epilogue 213(8)
6.1 Which type of quantity to choose?
213(4)
6.2 Exercises
217(4)
Bibliography 221(10)
Appendix: Answers to Exercises 231(14)
Index 245
Ronald B. Geskus is an associate professor at the Academic Medical Center in Amsterdam. He received a Ph.D. in mathematics from the Delft Technical University. His main research interests include competing risks and multi-state models, prediction of events based on time-updated marker values, and causal inference.
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