Course details

General
FacultyHealth Sciences
DepartmentMedicine
Education levelPostgraduate / Master of Science
Course codeC3Semester1
Course titleIntroduction to Statistics
Independent teaching activitiesHours per weekECTS
Lectures2
Practice6
Total88
CoursetypeGeneral setting course, skills development
Prerequisite coursesNone
Teaching and assessment languageEnglish

Learning outcomes

Objective

Upon completion of the course, the student will know how to apply methods from the basic statistics. He will be able to manage descriptive statistical measures for summary data and compare the results between populations using representative samples. Additionally, he will understand the uncertainty involved when a random estimation of populations’ parameter takes place and he will interpret and evaluate better the findings.
An additional objective of this course is the understanding of biostatistics methodology through practical guidance and use of the programming language R.

Knowledge

Upon completion of the course, graduate students will be familiar with:

  • The main differences between the types of studies of comparing populations
  • The appropriate summary measure of variables for quantitative and qualitative data
  • The difference between a sample and the population from which it came
  • The characteristics of the normal distribution and the difference from the asymmetric distribution
  • The sampling distribution and the concept of standard error of the mean
  • The methodology of hypothesis testing, the concepts of p value, the level of significance and confidence interval, the types of I and II errors
  • The basic parametric and non-parametric tests through real examples in the health and life sciences
  • In which data can be applied the survival analysis and how to conduct such an analysis
Skills

The course participants upon completion will be able to:

  • Understand and compute the descriptive statistical measures that appear in the medical scientific articles
  • Formulate and interpret graphs appropriately
  • Calculate association measures such as mean differences, risk differences, relative risks, odds ratios and incidence rate ratios related reason and impact-rates
  • Calculate the appropriate sample size of a survey
  • Use the fast-growing and evolving R project as a tool for statistical analysis and the creation of elegant graphs

Course contents

  1. Introduction to R
  2. Different types of data, quantitative and qualitative
  3. Summary measures for quantitative and qualitative data (practice in R)
  4. Graphs for quantitative and qualitative data (practice in R)
  5. The normal (Gaussian) distribution (practice in R)
  6. Measures of association:
    mean differences, risk differences, relative risks, odds ratios and incidence rate ratios
  7. Confidence intervals for measures of association
  8. Hypothesis testing- paired and two-sample t-tests:
    Mann-Whitney U test and Wilcoxon Signed Ranks test (practice in R)
  9. Hypothesis testing -tests for more than two samples:
    ANOVA and Kruskal-Wallistest (practice in R)
  10. Tests for categorical variables:
    χ^2, Fisher’s exact test, Mc Nemar’s test (practice in R)
  11. Survival analysis:
    Log-rank test and Kaplan-Meier plots (practice in R)
  12. Power and sample size calculation
  13. Module summary

Teaching and learning methods – evaluation

Teaching methodsFace to face
Distance learning
Use of information and
communication technologies (ICT)

  • Use of ICT in Teaching- Moodle Virtual learning environment (VLE)
    (asynchronous learning, wikis, Online Discussion Fora, Educational Portfolio, assignment submission, assessment process)

  • Use of ICT in Communication with students
    (email, instant messaging via Moodle)
Module structureWork Hours per SemesterActivity
Lectures 78
Exercises (Quiz) 20
Exercises (Wikis) 18
Exercises (Online discussion fora) 20
Exercises (Study relevant papers) 20
Essay background work64
Essay writing30
Overall work for the course250
Assessment Methods
  • Written assignment, in English, approximately 2,500 words long, to be submitted by each student at the end of the course

  • Assessment of knowledge at the beginning and the end of the course with short-answer questions and essays development

  • Weekly quizes, with multiple choice questions

  • Assessment based on comments submitted by each student in online discussion fora

Recommended Bibliography

  1. Aho, Ken A.Β Foundational and applied statistics for biologists using R. CRC Press, 2013.
  2. Bland, Martin.Β An introduction to medical statistics. 3rd Edition. Oxford University Press, 2000.
  3. Crawley, Michael J.Β Statistics: an introduction using R, 2nd Edition. John Wiley & Sons, 2014.
  4. Daniel, Wayne W., and Chad L. Cross.Β Biostatistics: A Foundation for Analysis in the Health Sciences: A Foundation for Analysis in the Health Sciences. Wiley Global Education, 2012.
  5. Kirkwood B, Sterne J. Essential Medical Statistics, 2nd Edition. Wiley, 2003.
  6. MacFarland, Thomas W. Introduction to Data Analysis and Graphical Presentation in Biostatistics with R. Springer, 2014.