Ateneo - UPO

Integrated teaching provides the theoretical and methodological knowledge necessary to understand, analyze, and interpret biomedical, clinical, epidemiological, and molecular data, integrating the principles of biostatistics, epidemiology, omics sciences, and Evidence-Based Medicine.
The main statistical methods applied to biomedical research, epidemiological study designs, genomic and transcriptomic data analysis techniques, and tools for the critical evaluation of scientific evidence will be addressed. Particular attention will be paid to interpreting results, assessing potential biases, and applying evidence in clinical and healthcare decision-making processes.
The teaching will also delve into the main methodological issues associated with high-dimensional data, bioinformatics analyses, predictive models, and the use of Artificial Intelligence systems in the biomedical field.

The course material will include handouts, presentations, datasets, R scripts, and scientific articles made available by instructors.

Reference and consultation texts:
Daniel WW, Cross CL Biostatistics: A Foundation for Analysis in the Health Sciences. 10th ed., Wiley.
Lash TL, VanderWeele TJ, Haneuse S., Rothman KJ Modern Epidemiology. 4th ed., Wolters Kluwer, 2021.
Strachan T, Lucassen A. Genetics & Genomics in the Medical Sciences. 2nd ed, Zanichelli.
Greenhalgh T, Dijkstra P. How to Read a Paper: The Basics of Evidence-Based Healthcare. 7th ed., Wiley-Blackwell, 2025.

The teaching aims to provide students with an interdisciplinary background that integrates statistical, epidemiological, molecular, and methodological knowledge for the analysis and critical interpretation of scientific information in clinical and public health settings.
Upon completion of teaching, students will have acquired the tools to understand the main study designs, select and apply appropriate statistical methods, interpret genomic data, and critically evaluate the quality of scientific evidence.
The teaching also aims to develop autonomy of judgment in interpreting biomedical research findings, assessing their clinical relevance, and analyzing the benefits, risks, and uncertainties associated with healthcare decisions, with particular attention to the applications of predictive models and Artificial Intelligence.

Basic knowledge of biology, genetics, molecular biology, probability, and descriptive statistics is useful.
In view of the heterogeneity of the training courses, the main concepts needed will be recalled during the lessons. A prior knowledge of R software is useful but not indispensable.

Teaching includes lectures, theoretical and practical activities, guided exercises with R software, analysis of biomedical and epidemiological datasets, and critical discussion of scientific articles.

Application examples, tables, graphs, molecular analysis results, and clinical scenarios will also be used, with the aim of promoting the integration of knowledge and the development of interpretative and critical skills.

The teaching materials and any tools for the exercises will be made available through the course's teaching platform on DIR. For practical biostatistics activities, the availability of a computer with R and RStudio installed is recommended.
Female students and students with disabilities, DSA, or BES can refer to the University's dedicated services and contact faculty for teaching aspects and learning assessment methods.

The learning assessment will aim to assess understanding of statistical, epidemiological, and molecular principles, as well as the ability to critically interpret scientific data and findings and use evidence in healthcare research and decision-making.
Evaluations will include theoretical and application questions, interpretation of tables, graphs, statistical outputs, results of genomic and transcriptomic analyses, and critical evaluation of the scientific literature.
The overall arrangements for conducting the examination and determining the final grade will be defined at the integrated teaching level.

1. Foundations of biostatistics and epidemiology
Descriptive and inferential statistics, data distributions, point and interval estimation, hypothesis testing. Main epidemiological study designs, measures of frequency and association, bias, confounding, and effect modification. Linear, logistic, Poisson, and Cox regression models. Survival analysis using Kaplan-Meier curves. Analysis and interpretation of biomedical and epidemiological data using R software.

2. Statistical methods for complex, high-dimensional data
Characteristics of omics data, biological and technical variability, preprocessing and normalization. Exploratory analysis, issues associated with high dimensionality, multiple comparisons, and control of the False Discovery Rate. General principles of biomarker identification and interpretation.

3. Genetics, genomics and molecular omics sciences
Fundamentals of genetics and genomics, genome structure, DNA replication, and the flow of genetic information. Polymorphisms and mutations. Sanger PCR and sequencing techniques and Next Generation Sequencing. Whole exome sequencing, whole genome sequencing, and panel sequencing. Genomic data analysis pipeline: alignment, identification, and annotation of variants. Genotyping techniques and platforms. Genome-Wide Association Studies (GWAS), analysis methodologies. Techniques of transcriptomics, epigenetics and DNA methylation analysis.

4. Evidence Based Medicine and critical evaluation of evidence
Principles of Evidence Based Medicine and Evidence Based Healthcare. Research question formulation and PICO model. Hierarchy of evidence, appropriateness of study designs, internal and external validity, critical evaluation of scientific literature. Randomized clinical trials, benefit and risk measures, diagnostic and screening studies. Systematic reviews, meta-analyses, clinical guidelines and recommendations.

5. Clinical Decision Analysis and Applications of Artificial Intelligence
Principles of decision analysis under uncertainty. Evaluation of alternatives, probabilities, consequences, benefits and risks. Introduction to decision trees, the concept of utility, and sensitivity analysis. Critical evaluation of predictive models and AI systems applied to biomedical research and healthcare, with a focus on validation, generalizability, bias, safety, and clinical applicability.

At the end of teaching, students will be able to:

Understand and interpret the main statistical and epidemiological methods applied to biomedical and clinical research.
Recognize key study designs, measures of association, and potential sources of bias and confounding.
Select and interpret statistical models appropriate to the data structure and scientific demand.
Understand the principles and applications of key genomic and transcriptomic technologies, including Next Generation Sequencing techniques and GWAS studies.
Critically interpret results from molecular, bioinformatics, and high-dimensional data analyses.
Evaluate the methodological quality, robustness, and applicability of scientific evidence.
Interpret the main measures of benefit, risk and diagnostic accuracy.
Understand the principles of decision analysis and critically evaluate the evidence supporting predictive models and AI systems in healthcare.
Communicate and discuss scientific findings using appropriate language.