KLMED8004

Introduction to Medical Statistics

Autumn

Trondheim

English

Overview

38 candidates

Pass rate

89%

7 points

Grade distribution
Average over time
Pass rate over time

About this course

Content

This course gives an introduction to basic concepts and main principles of statistical analyses of empirical data, in addition to theory and application of descriptive measures and analytical methods often used within medical research. The course covers statistical methods for comparing groups with respect to mean values of a continuous variable (T-tests and one-way ANOVA - ANnalysis Of VAriance, alternative non-parametric methods), values of a categorical variable (Chi-square and McNemar’test), and methods for evaluation of linear association between two continuous variables (correlation and simple linear regression analysis). A single measure for quantification of the association between two categorical variables (odds ratio) and methods for evaluation of degree of agreement (equality) in measurements (Kappa coefficient, Bland-Altman plots) are also part of the syllabus of this course.

Learning outcomes

Knowledge

After successful completion of this course the student should

  • understand the main principles of statistical analyses of empirical data
  • have achieved theoretical knowledge about statistical methods covered by this course

Skills

After successful completion of this course the student should be able to

  • choose the most appropriate statistical method in view of scientific question of interest, study design and characteristics of the empirical data
  • perform statistical analyses of data by means of a statistical program package
  • interpret, describe and present results from statistical data analyses

General competence

After completion of this course the student should have

  • achieved sufficient theoretical knowledge and practical skills to be able to perform the statistical analyses of data and report results in relation to own research work

Teaching methods

Lectures and exercises, including technical work with data analyses (by means of SPSS or STATA). The work with data analyses includes both guided sessions and homework.