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Applied statistics III – Survival analysis

Course lecturer
Philippe Wagner, Statistician philippe.wagner@med.lu.se

Course leaders 
Jonas Björk, professor of Epidemiology, jonas.bjork@med.lu.se
 
Target group
The course is intended for doctoral students at the medical faculty with research projects suited for survival analysis. The course is, conditional on availability, also intended for post-doctoral students and others with a special interest in survival analysis at the medical faculty and for doctoral students, post-doctoral students and others with special interest outside of the medical faculty. Although the latter may consitute at most 50% of course participants. All course attendants must have prior knowledge in applied statistics corresponding to course levels I and II. Access to and substantial skills in a statistical software packages are also required: SPSS, SAS, STATA, R or equivalent.

Time
Lectures are on the 21-25 January 2019 

Participants
20

Place 
Lund - Lecture room will be announced to the students before course start.

Course content
1. Introduction
a. Definitions
b. Typical situations suitable for survival analysis
c. Study design

2. Description of data
a. The survival function
b. Life tables
c. The Kaplan-Meier estimate

3. Analyses of group differences
a. The Kaplan-Meier estimate and confidence interval
b. The log-rank test

4. Analyses of group differences – in depth
a. Confounding
b. The stratified log-rank test
c. The Hazard rate function
d. The Cox model and the hazard rate ratio
e. The adjusted hazard rate ratio
f. Fitting the Cox model
g. Log-minus-log-plot
h. Schoenfeld residuals

5. Cox-regression – more theory 
a. Sampling from the risk set - similarities with the nested case-control design
b. Similarities between the hazard rate ratio and incidence rate ratio

Course literature
Articles and additional course material will be announced before and during the course.

The course will be held in English and requires active participation and access to laptop with installed statistical package that you are familiar with from earlier courses (SPSS, STATA, SAS, R or equivalent). More information will be provided before the course start.

Application form

Deadline for application for courses spring 2019: October 15

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