Biostatistics

The aim of the course is to provide students with knowledge of basic and advanced statistical methods for problem-solving through data analysis in various scientific fields, such as Biology, Medicine, and Life and Health Sciences. The expected learning outcomes include acquiring knowledge of fundamental concepts in the scientific domain of Statistics and Probability (population, sample, discrete and continuous probability distributions, etc.), as well as basic methods of Descriptive and Inferential Statistics (parametric and non-parametric hypothesis tests). In addition, particular emphasis will be placed on well-known Biostatistical methods for knowledge discovery in multivariate data and modeling the relationships between a dependent variable and a set of independent variables using Regression Models (Linear Regression, Logistic Regression, etc.) and Survival Analysis. To facilitate the understanding of these methods and the extraction of meaningful conclusions, the course will focus on the detailed presentation of applications and case studies using real data from Biology, Medicine, and Life and Health Sciences. Students will also acquire practical skills in implementing these methodologies and techniques in the open-source programming language R, developing code within the RStudio Integrated Development Environment (IDE).

Upon successful completion of the course, students will be able to:

• Understand the fundamentals of Biostatistics and its applications in Biology, Life Sciences, and Health Sciences.
• Describe populations and samples, compute descriptive statistics, and perform exploratory data analysis and multivariate visualizations using R.
• Understand and apply probability distributions, including Normal, t-student, Binomial, and Poisson distributions, in Biostatistical contexts.
• Construct confidence intervals and perform hypothesis testing, including parametric and non-parametric tests, and interpret Type I and Type II errors.
• Apply parametric tests such as t-tests, ANOVA, and tests for categorical data, using R for practical case studies.
• Perform non-parametric tests, including Wilcoxon signed-rank, Mann-Whitney U, and Kruskal-Wallis tests, with applications in real datasets.
• Conduct correlation and regression analyses (simple and multiple linear regression), check assumptions, perform diagnostics, and interpret models in R.
• Apply logistic and Poisson regression for binary, categorical, and count data using R.
• Understand clinical study designs and apply statistical methods for analyzing data from experimental and observational studies.
• Use advanced methods including generalized linear models, mixed-effects models, hierarchical ANOVA, and repeated measures analysis in practical case studies.
• Perform survival analysis using Kaplan-Meier estimators, log-rank tests, and Cox proportional hazards models with R.
• Apply statistical methods in genomics, including GWAS data analysis, Hardy-Weinberg equilibrium, and SNP association studies using R.