Duration data analysis

This course is concerned with analysis of duration data - also known as survival analysis.

The textbook for the course is KM:

The course will be evaluated (pass/fail) by active participation in solving exercises and miniproject and presenting solutions of exercises.

Vemmetofte data and Thieles report concerning establishing a "klosterforsikring" (materiale venligst stillet til rådighed af Professor Steffen L. Lauritzen).

Catalogue of exercises (updated 05.09.24).

Malignant melanoma data and project formulation regarding these data.

Cirrhose data and description of these data.


1. September 9 8.15-10 Slides (updated 06.09.24). We will consider examples of duration data and discuss particular aspects of such data. Read KM Chapter 1. Solve exercises 4, 5 and 7 in catalogue of exercises.

2. September 16 8.15-10 Slides (updated 12.09.24). Estimation of the survival function and the cumulative hazard. Read KM 4.1-4.3. Solve exercises 6, 8, 10, 11 in exercise catalogue. Code for simulation study (updated 12.09.24). Lidt om regning med infinitesimale størrelser (updated 12.09.24).

3. September 23 (selfstudy) basic concepts: survival function, hazard function, mean residual life time. Read KM 2.1-2.4. Solve exercises 1, 2, 3, 9 and 12 in catalogue of exercises. Further, solve exercises 4.1 (a)-(d) and exercise 4.5 (a)-(c) in KM (you're welcome to use R survfit() - check documentation via help(survfit.formula)). Data from KM are available in the R-package KMsurv.

4. September 30 8.15-10 Lecture Cox's proportional hazards model (excluding slides on case of data with ties) (updated 29.09.24). Video lecture "asymp ties" (on youtube channel): Cox PH in case of data with ties and Cox's model for discrete time data. Read KM 8.1-8.4. Solve exercise 14, 15, 17, 18.

5. October 7 8.15-10 Groups present exercises. Also selfstudy this week regarding censoring and likelihoods. Read KM 3.1-3.5. Solve exercises 3.1, 3.3, 3.7 (a) and 3.8 in KM.

6. October 14 8.15-10 Lecture on model assessment (updated 12.10.22). Code for an example of model assessment. Code for Andersen plot and plots of cumulative hazards for stratified model. Show results regarding distributions of S(X) and H(X) on Cox-Snell slide. Solve 19 and 23 in exercise catalogue. Also give a detailed account of the profile likelihood approach for Cox's partial likelihood. Read KM 8.5, 8.8, 9.3 and 11.1-11.6.

Miniproject: Work on miniproject during weeks 43 and 46.

7. October 21 28 8.15-10 Groups present exercises.

8. October 28 8.15-10 We consider a simulation study of model assessment. Code for simulation study of model assessment. We next consider first 15 slides in counting processes (updated 04.11.24). Read KM section 3.6. You may also take a look at this paper. Solve exercises 1 and 2.1 in counting process slides and exercise 3.9 in KM.

9. November 4 8.15-10 We finish counting processes. Review of counting processes and martingales. Solve exercise 2.2 and 3 in counting process slides.

10. November 18 8.15-10 Counting processes and time-varying covariates. Slides on time-dependent covariates (updated 16.11.20). Code for analysis of bone marrow transplant data. Some advice on timedependent covariates in R. Read KM sections 9.1-9.2. Solve KM exercises 9.1 and 9.3 (note: you can use the tt() functionality for this) and exercise 27 in exercise catalogue.

We also start considering frailty (updated 16.11.20) models. Start solving exercises from frailty slides.

11. November 25 8.15-10 We continue with frailty models. Some code for frailty models. Solve remaining exercises from frailty slides. Read KM 13.1, 13.3, 13.4. Next groups present exercises and we discuss aspects of miniproject.

12. December 2 8.15-10 Groups present remaining exercises. We also consider competing risks (updated 18.11.22). Read KM 2.7. Note on competing risks. Start selfstudy (continued December 5).

13 December 5 10:15 selfstudy: parametric models and parametric inference. Read KM 2.5-2.6 and KM 12.1-12.5. KM exercises 2.1, 2.3, 2.9, 2.13, 2.15 (try in exercise 2.9 b) to replace 2 in front of W with 2/1.8 where 1.8 is the standard deviation of W), KM12.1, KM12.9, KM12.13.

Continuation of analysis for cirrhosis data: try to apply a parametric model for the data (survreg() procedure in R). Can you identify a suitable parametric model by considering the estimate of the baseline cumulative hazard H_0 fitted under the Cox regression model ? Compare the results with the Cox regression results.

13. December 9 8.15-12 We start by brief review of parametric models. Next presentation of exercises regarding parametric models and exercises from November. Opsummering af kursets indhold.


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