Biomedical Statistics
Objectives
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
Code
10811
Credits
6.0
Responsible teacher
Isabel Cristina Maciel Natário
Hours
Weekly - 4
Total - 56
Teaching language
Português
Prerequisites
Basic notions of Probability and Statistics.
Bibliography
- 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)