course-details-portlet

KLMED8019 - Survival analysis

About

Examination arrangement

Examination arrangement: Home examination
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Home examination 100/100 10 days

Course content

This course cover standard statistical methods for analysis of failure- or survival time data (Kaplan-Meier cumulative survival probabilities, Nelson-Aalen cumulative hazard estimate, and the semi-parametric Cox proportional hazard regression model). Simple calculations of cumulative survival probabilities and Nelson-Aalen estimate will be covered. For the Cox PH regression model, focus will be on application, including evaluation of inherent assumptions on the regression model, and what do if the assumptions are not met. Choice of time scale, use of time-dependent covariates, and modelling of time-dependent effects in a parametric survival analysis model (Poisson regression analysis), will also be covered by this course. Survival analyses techniques are appropriate to apply for all types of follow-up studies, including randomized, controlled clinical trials and population-based, prospective epidemiological studies. The Cox PH regression model is often used in studies based on data from HUNT and other public health surveys. The aim of these types of studies is often to identify risk or prognostic factors, or the interaction between different exposure factors.

Learning outcome

Knowledge

After successful completion of this course, the student should

  • have gained sufficient theoretical knowledge to be able to correctly apply the statistical methods and models covered by this course in a research project on PhD level

Skills

The student should

  • be able to select an appropriate method or model to evaluate simple and more complex scientific questions within medicine and health science
  • be able to independently perform the statistical data analyses by means of a statistical program package
  • be able to evaluate the assumptions on the applied method or model
  • be able to interpret and critically evaluate the result from the statistical analyses
  • be able to present the results from the statistical data analyses in a format applicable in a scientific medical journal

General competence

The student should

  • be able to apply, evaluate and discuss application of the statistical methods covered by this course in medical research projects

Learning methods and activities

Lectures and exercises. Practical data analyses by means of SPSS and/or STATA.

Course materials

Lecture notes and other learning material posted by teacher.

Textbook by Kirkwood and Sterne 2003; Essential medical statistics, second edition

Credit reductions

Course code Reduction From To
KLMED8005 1.5 AUTUMN 2022
More on the course

No

Facts

Version: 1
Credits:  3.0 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Medicine
  • Statistics
Contact information
Course coordinator:

Department with academic responsibility
Department of Public Health and Nursing

Timetable

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Examination

Examination arrangement: Home examination

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Home examination 100/100 INSPERA
Room Building Number of candidates
Spring UTS Home examination 100/100 INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

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