28172 - BIOSTATISTICS

Learning outcomes

At the end of the course, the student knows the applied statistical techniques. In addition to acquiring basic knowledge of descriptive statistics, the student understands the logic of statistical inference and is able to apply the most common statistical tests in research and professional activity. The student is also able to perform statistical analyses with dedicated software and interpret the output in the context of the analyzed phenomenon or experiment.

Course contents

The course introduces fundamental and intermediate statistical methods, with applications to biomedical engineering. Topics include:

• Biomedical data, types of variables and measurement scales
• Experimental and observational study designs, bias, confounding and causal interpretation
• Contingency tables, risk measures, odds ratios and Simpson’s paradox
• Descriptive statistics, data visualization and exploratory data analysis
• Probability, probability distributions and diagnostic test evaluation
• Sampling distributions, estimation and confidence intervals
• Hypothesis testing, effect sizes, statistical power and practical significance
• Simple and multiple linear regression, including confounding, interactions and model diagnostics
• Logistic regression for binary outcomes
• Analysis of variance and comparison of multiple groups
• Time-to-event data and survival analysis
• Poisson and negative binomial regression for count data
• Introductory cluster analysis

Real and realistic biomedical datasets will be used throughout the course. Emphasis will be placed on statistical reasoning, selection of appropriate methods, interpretation of results, and assessment of model assumptions. All analyses will be conducted using R.

Readings/Bibliography

Main textbook

Wayne W. Daniel and Chad L. Cross, Biostatistics: A Foundation for Analysis in the Health Sciences, 11th Edition, Wiley.

This is the main reference textbook for probability, statistical inference, analysis of variance, linear and generalized linear models, analysis of categorical data and survival analysis. Specific chapters and sections will be indicated during the course.

Supplementary reference

Ronald N. Forthofer, Eun Sul Lee and Mike Hernandez, Biostatistics: A Guide to Design, Analysis, and Discovery, 2nd Edition, Academic Press.

Selected sections will be used to complement the main textbook, particularly for study design, data quality, bias, confounding and interpretation of biomedical studies.

Additional materials and R scripts for topics not fully covered by the main textbook will be provided through the course platform.

Teaching methods

The course combines lectures, guided problem-solving and weekly hands-on laboratory sessions using R.

Lectures will introduce statistical concepts through biomedical questions, examples and case studies. Guided exercises will be used to develop statistical reasoning and interpretation skills. During laboratory sessions, students will analyze biomedical datasets, implement the methods introduced in class, assess model assumptions and interpret R output.

Theoretical and computational activities will be closely integrated throughout the course.

Assessment methods

Assessment consists of a final written examination including theoretical questions, applied exercises and the interpretation of statistical analyses and R output.

The examination assesses the student’s ability to select an appropriate statistical method, explain the underlying assumptions, perform or interpret the analysis, and communicate conclusions in the context of a biomedical problem.

 

Students with Specific Learning Disorders (SLD) or temporary or permanent disabilities are encouraged to contact the University’s dedicated support office as early as possible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en). The office will propose any appropriate accommodations for the students concerned. These accommodations must be submitted to the course instructor for approval at least 15 days in advance. The instructor will assess their suitability, taking into account the intended learning outcomes of the course.

Teaching tools

Lecture notes, selected textbook readings, datasets, guided exercises and R scripts will be provided through the course platform.
Statistical analyses will be performed using R and RStudio. Students are expected to have access to a laptop during laboratory sessions.

Office hours

See the website of Cinzia Viroli