The textbook for the course is KM:

- Klein and Moeschberger (2003) Survival analysis - techniques for censored and truncated data, second edition.

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.

Last modified