spring 2017
HEL-8002 Logistic Regression and Statistical Analysis of Survival Data - 3 ECTS

Last changed 28.03.2017

Application deadline

External applicants: Application deadline is December 1st for spring semester and June 1st for autumn semester. Application code 9303 in Søknadsweb.

If granted admission to the course you have to register for class and exam in Studentweb by February 1st for spring semester and September 1st for autumn semester.

PhD students and students at the Medical Student Research Program at UiT - The Arctic University of Norway go directly to Studentweb to register for class and exam.

Type of course

PhD Course. This course is available as a singular course.

Admission requirements

PhD students, Masters students at a Medical Student Research Program, or holders of a Norwegian master´s degree of five years or 3+ 2 years (or equivalent) may be admitted. Valid documentation is a statement from your institution that you are a registered PhD student, or master student at a Medical Research Program or a Master´s Diploma with Diploma Supplement / English translation of the diploma. PhD students are exempt from semester fee.

For more information regarding PhD courses at the Faculty of Health Sciences go to: http://uit.no/helsefak/forskning/phd/emner

Admission recommendations:

Introductory course in medical statistics. It is recommended that students who are planning to take HEL-8024 complete it before taking this course.

Course overlap

Du vil få en reduksjon i antall studiepoeng (som oppgitt under), dersom du avlegger eksamen i dette emnet og har bestått følgende emne(r) fra før av:

MED-8002 Statistical analysis of survival data 1 stp
MED-8004 Logistic regression 1 stp

Course content

Two main topics are covered:
  • Simple, multiple and stepwise logistic regression, matched case-control studies, and ordinal logistic regression
  • Methods for analysis of survival data. Includes the Kaplan-Meier survival estimator, the log rank test, and Cox's Proportional Hazard regression model.

Objectives of the course

  • Analyze data using logistic regression models and models for the analysis of survival data (included Kaplan-Meier survival function, log rank test, and Cox proportional hazard regression).
  • Identify different types of explanatory variables and correctly implement them in a logistic or Cox regression model.
  • Interpret the results from logistic or Cox regression models.
  • Assess interaction and confounding.
  • Evaluate the model assumptions.
  • Evaluate results from publications in medical journals where logistic or Cox regression models are applied, and critically assess the validity of its use.

Language of instruction and examination


Teaching methods

The program consists of lectures, exercises with the use of PC and review of tasks. One can choose between the program packages SPSS, STATA and SAS.


Exam and evaluation
The exam consists of a written assignment to be submitted 14 days after the assignment is distributed. Evaluated with pass / fail.

Continuation exam
The course is held annually, no continuation exam.

Recommended reading/syllabus

The curriculums in the course are the compendium that largely are lecture notes. However, you will find out more about the methods and SPSS / STATA in the books and articles below.


SPSS (not survival analysis): Field A: Discovering Statistics Using SPSS.

SPSS (include survival analysis): Norusis MJ: PASW Statistics 18 Advanced Statistical Procedures (earlier version SPSS 16.0 Advanced Statistical Procedures Companion).

STATA: S. Juul: An Introduction to Stata for Health Researchers

DG Kleinbaum: Survival Analysis. A Self-Learning Text: Kap 1-5.

Kleinbaum, Kupper, Muller, Nizam: Applied Regression Analysis and Multivariate Methods: (mainly for HEL-8001 but includes a chapter on logistic regression)

DG Kleinbaum: Logistic Regression. A Self-Learning Text: Kap. 1, 2, 6.I-6.III, 8.I, 8.II, 8.IV.

  • About the course
  • Campus: Tromsø |
  • ECTS: 3
  • Course code: HEL-8002
  • Tilleggsinformasjon
  • PhD students at UiT
  • External applicants
  • Exam
  • Course information