Biostatistics

10 ECTS / Annual / Portuguese

Intended learning outcomes (knowledge, skills and competences to be developed by the students):

Development of the autonomous ability to use statistics, as well as data analysis tools, with a particular focus on the biomedical context.

It is intended, with this Curricular Unite, to ensure the independence of students in the field of scientific research, in terms of: i) formulation of hypotheses and scientific questions in a statistically coherent and consistent way; (ii) design of the studies necessary to obtain the respective answers; iii) execution of these studies, knowing how to treat and analyze the data at progressively deeper levels; iv) understanding of the limitations of the tools used, having the critical ability to analyze data from various perspectives; v) ability to analyze results and draw statistically relevant and valid conclusions; and vi) knowledge of scientific writing of statistical methods and results, as well as their understanding and analysis. 

Syllabus:

Module 1: Descriptive statistics and exploratory data analysis

  • Qualitative analysis and scientific thinking;
  • Variables and scientific questions;
  • Sampling;
  • Data visualization;
  • Basic distributions;
  • Parameters and estimation;
  • Exploratory data analysis;
  • Introduction to SPSS.

Module 2: Inferential Statistics I

  • Effects and errors;
  • Confidence intervals;
  • Hypothesis tests (mean, proportion and dependence);
  • Normality tests;
  • Parametric vs. non-parametric tests;
  • Risk analysis;
  • Diagnostic tests and ROC curves;
  • Application in SPSS.

Module 3: Inferential Statistics II

  • Dependence and covariance;
  • Linear regression;
  • Logistic regression;
  • Survival analysis (Kaplan-Meier and Cox);
  • ANOVA;
  • ANCOVA;
  • GLM;
  • Application in SPSS.

Module 4: Data analysis

  • Data processing;
  • Reduction of parametric space;
  • Distance measurements;
  • Data classification - supervised and unsupervised;
  • Bayesian algorithms;
  • Clinical applications;
  • Introduction to R.