Introduction to Biostatistics

Prerequisites (knowledge of topic)
-    This is an introductory course to Biostatistics (basic principals will be introduced)
-    However, basic knowledge on data types (binary data, categorical data and numerical data) and measures of data distribution (mean, model, median, normal distribution, skewed data) is highly recommended.

-    Laptop (Windows, macOS or Ubuntu)


-    R-Studio, Freeware available at

Course content
This course will introduce basic concepts of different study designs and how they relate to evidence-based health care. Participants will learn to understand and interpret the most relevant study designs, their clinical use and limitations. They will also learn the basic concepts of bias and confounding and learn strategies to detect these limitations and assess the quality of published research.
Students will apply their learning in practical sessions and during supervised small group activities for which they will receive constructive academic feedback.

This course will enable students to:
-    Understand basic bio-statistical principals
-    Describe different types of research methods and study designs
-    Identify the strengths and weaknesses of the different study design

This course will cover the biostatistics content of the Swiss Catalogue of Learning Objectives (SCLO) for medical training (C PH 5 - 16).

Day 1
-    Data types and Distributions
-    Standard deviation, confidence intervals, p-values and more
-    Overview of study designs
-    Bias, confounding and chance
-    Randomised controlled trials 

Day 2
-    Cohort studies
-    Univariate and multivariate linear regression (including practical R-Session)
-    Case control studies

Day 3
-    Systematic reviews & Meta-Analysis
-    Logistic regression (including practical R-Session
-    Small group activities

Day 4
-    Diagnostic accuracy tests
-    Sample size and power
-    Correlation and agreement
-    Small group activities

Day 5
-    Time-to-event data
-    Survival analysis (including practical R-Session)
-    Repetition of the week
-    Presentation of Results from small group activities

Supplementary / voluntary readings before the course starts
Epidemiological Studies - A practical guide by A.J. Silman and G.J. Macfarlane
A very concise and well-structured introduction to epidemiology. This is not a statistical textbook but it provides essential basics that are prerequisite to apply statistical methods.   

Essential Medical Statistics by B.R. Kirkwood and J.A.C. Sterne
A more technical but still worth reading, especially the parts A to E. This textbook covers the entire statistical content of this introductory course and goes beyond.

How to read a paper - The basics of evidence-based medicine
by T. Greenhalgh
A must-read for everyone who is interested in Evidence-Based Medicine and critical appraisal of published data.

The free available statistical notes in the British Medical Journal by Dough Altman and Martin Bland:
Especially recommended:
- No 10: The normal distribution
- No 13: Absence of evidence is not evidence of absence
- No 15: Presentation of numerical data
- No 36: Time to event (survival) data
- No 42: The odds ratio

Examination part
Students will be required to submit a written assignment within 3 weeks after the course. The performance of the participant’s written assignment compiles the final grade (=100%). The assignment requires an estimated 10h workload.

Examination content (=the SCLO Content C PH 5 - 16)
The student should know:

Measures of association:
-    Relative measures (risk ratio, rate ratio, odds ratio)
-    Absolute measures (risk difference, number needed to treat / harm, attributable ‚Ä®risk)

Observational and experimental study designs, their advantages and disadvantages and areas of application:
-    Case series
-    Cross-sectional studies
-    Case-control studies
-    Cohort studies
-    Randomized controlled trials
-    Systematic reviews
-    Meta-analysis

Critical appraisal of study methodology, internal and external validity of results:
-    Systematic error (selection bias, information bias, lead time bias, length bias, over-diagnosis bias)
-    Confounding and how to deal with

Diagnostic and screening tests:
-    Sensitivity, specificity
-    Positive and negative predictive values, likelihood-ratio
-    Pre-test probability, post-test probability.

Types of variables:
-    Categorical (binary, nominal, ordinal) and numerical (discrete, continuous).

Describing data and their variability:
-    Frequency, proportion, mean, standard deviation, median
-    Histogram, box-plot, scatter plot, survival curve.
-    Standard errors
-    Confidence intervals.

Principals of hypothesis testing:
-    Null and alternative hypotheses
-    Interpretation of P values
-    Relation between P values and confidence intervals.

Literature to solve the assignment
The entire content is freely available online on various websites. However, these two books will cover the entire content required to solve the assignment:

Epidemiological Studies - A practical guide
by A.J. Silman and G.J. Macfarlane

Essential Medical Statistics by B.R. Kirkwood and J.A.C. Sterne