Biomedical Statistics


This course appears in cutting-edge scenario of applications of statistical techniques and although mainly directed to those interested in applications in the area of health it may interest a multitude of other areas.

General characterization





Responsible teacher

Isabel Cristina Maciel Natário


Weekly - 4

Total - 56

Teaching language



Basic notions of Probability and Statistics.


- Carvalho, L. & Natário, I. (2008). Análise de dados espaciais. SPE.

- Turkman, M.A.A., Silva, G.L. (2000). Modelos lineares generalizados - da teoria à prática. SPE.

- Cressie, N.A.C. (1993). Statistics for spatial data. Wiley, 2nd Edition.

- McCullagh, P. & Nelder, J.A. (1989). Generalized linear models. Chapman & Hall/CRC, 2nd Edition.

- Zhou, X-H et al. (2011). Statistical methods in diagnostic medicine. Wiley, 2nd Edition.

- Cabral, M.S. & Gonçalves, M.H. (2010). Análise de dados longitudinais. SPE.

- Rocha, C. & Papoila, A.L. (2009). Análise de sobrevivência. SPE.

- Diggle, P. et al. (2002). Analysis of longitudinal data. Oxford University Press, 2nd Edition.

- Klein, J.P. & Moesschberger, M. (2003). Survival analysis. Springer, 2nd Edition.

- Collett, D. (1994). Modelling survival data in medical research. Chapman & Hall.

- Paulino, C.D., Turkman, M.A.A. & Murteira, B. (2003). Estatística Bayesiana. Fundação Caloust   Gulbenkian. 

- Bernardo J.M. & Smith, A.F.M. (1994). Bayesian theory. Wiley.

- Gamerman, D. & Lopes, H.F. (2006). Markov chain Monte Carlo - stochastic simulation for   Bayesian inference. Chapman & Hall/CRC.

- Crawley, M.J. (2007). The R book. Wiley.

Teaching method

- 2 hours per week in a single block for class/lab regarding the methodologies;

- 2 hour per week in tutorial for computational implementation and analysis with the software R, presentation/discussion of assignments.

Evaluation method

Assignments and/or tests by module and/or final exam, according to the characteristics of the modules.

Subject matter

Module I – Statistical methods in Epidemiology

  • Generalized Linear Models (logistic, Poisson, Gaussian and gamma regression, etc.)
  • ROC (Receiver Operating Characteristic) methodology
  • Spatial statistics 

Module II – Longitudinal data and survival analysis

  • Longitudinal data
    • Longitudinal data exploration
    • Mixed Linear Model
    • Random effects models
    • Estimation
  • Survival Analysis
    • Non-parametric estimation
    • Cox regression model
    • Parametric survival models
    • Frailty models


Module III – Introduction to Bayesian statistics

  • The Bayesian paradigm
  • Prior and Posterior distributions
  • Bayesian inference
  • Sequential estimation and Markov Chain Monte Carlo (MCMC) 


Programs where the course is taught: