- Title Pages
- Preamble
- Acknowledgments
- 1 A Framework for Investigating Change over Time
- 2 Exploring Longitudinal Data on Change
- 3 Introducing the Multilevel Model for Change
- 4 Doing Data Analysis with the Multilevel Model for Change
- 5 Treating TIME More Flexibly
- 6 Modeling Discontinuous and Nonlinear Change
- 7 Examining the Multilevel Model’s Error Covariance Structure
- 8 Modeling Change Using Covariance Structure Analysis
- 9 A Framework for Investigating Event Occurrence
- 10 Describing Discrete-Time Event Occurrence Data
- 11 Fitting Basic Discrete-Time Hazard Models
- 12 Extending the Discrete-Time Hazard Model
- 13 Describing Continuous-Time Event Occurrence Data
- 14 Fitting Cox Regression Models
- 15 Extending the Cox Regression Model
- Notes
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 10
- Chapter 11
- Chapter 12
- Chapter 13
- Chapter 14
- Chapter 15
- References
- Index
Fitting Cox Regression Models
Fitting Cox Regression Models
- Chapter:
- (p.503) 14 Fitting Cox Regression Models
- Source:
- Applied Longitudinal Data Analysis
- Author(s):
Judith D. Singer
John B. Willett
- Publisher:
- Oxford University Press
This chapter describes the conceptual underpinnings of the Cox regression model and demonstrates how to fit it to data. Section 14.1 begins by developing the Cox model specification itself, demonstrating why it is a sensible representation. Section 14.2 describes how the model is fit. Section 14.3 examines the results of model fitting, showing how to interpret parameters, test hypotheses, evaluate goodness-of-fit, and summarize effects. Section 14.4 concludes by presenting strategies for displaying results graphically.
Keywords: continuous-time hazard, Cox regression model, model fitting, parameters, hypotheses, goodness-of-fit
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- Title Pages
- Preamble
- Acknowledgments
- 1 A Framework for Investigating Change over Time
- 2 Exploring Longitudinal Data on Change
- 3 Introducing the Multilevel Model for Change
- 4 Doing Data Analysis with the Multilevel Model for Change
- 5 Treating TIME More Flexibly
- 6 Modeling Discontinuous and Nonlinear Change
- 7 Examining the Multilevel Model’s Error Covariance Structure
- 8 Modeling Change Using Covariance Structure Analysis
- 9 A Framework for Investigating Event Occurrence
- 10 Describing Discrete-Time Event Occurrence Data
- 11 Fitting Basic Discrete-Time Hazard Models
- 12 Extending the Discrete-Time Hazard Model
- 13 Describing Continuous-Time Event Occurrence Data
- 14 Fitting Cox Regression Models
- 15 Extending the Cox Regression Model
- Notes
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 10
- Chapter 11
- Chapter 12
- Chapter 13
- Chapter 14
- Chapter 15
- References
- Index
