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[ms_accordion_item title=”Teaching Staff” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Armando Rogério Martins Teixeira Pinto
Hernâni Manuel da Silva Lobo Maia Gonçalves
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Teaching language” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Portuguese – Suitable for English-speaking students
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Objectives” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
- Explain the theoretical and practical foundations of applying advanced statistical methods applied to clinical research, health services and health technology assessment;
- Extend the concept of linear regression to generalized linear models (GLM);
- Use specific methods for time data until the event (survival analysis);
- Interpret multivariable GLM results, in particular linear regression and logistic regression, including interaction between variables;
- Integration of statistical and clinical points of view in the development and evaluation of regression models;
- Explain the concepts of Poisson regression and the fundamentals of missing data, “propensity scores” and longitudinal data.
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[ms_accordion_item title=”Learning outcomes and competences” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
At the end of this course, students should acquire knowledge and understanding of the characteristics and assumptions of the advanced statistical methods covered. With these learning results, students should acquire skills in identifying the most appropriate method (s) for a given situation and be able to carry out a critical understanding of the results obtained.
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[ms_accordion_item title=”Working method” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Presencial
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Program” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
- Multivariable linear regression analysis;
- Simple and multivariate logistic regression analysis;
- Development and evaluation of linear and logistic regression models;
- Survival analysis;
- Introduction to the concepts of Poisson regression, missing data, “propensity scores” and longitudinal data.
We emphasize that the PhD Thesis Project UCs I, II work autonomously. However, there is a sedimentation carried out sequentially and progressively in the knowledge, skills and competences acquired in each of the UCs
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[ms_accordion_item title=”Teaching methods and learning activities” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
ATeaching methods:
Theoretical analysis of each topic described for the course;
Literature review
Discussion of scientific papers
Individual exercises
Using an optimized platform for e-learning for teaching the topics taught in the course.
Evaluation methods:
Distributed evaluation with final exam. The evaluation will be conducted using written work (30%) and a final exam (70%).
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[ms_accordion_item title=”Evaluation Type” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Distributed evaluation without final exam
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[ms_accordion_item title=”Mandatory literature” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Pagano, M. & Gauvreau, K.; Principles of biostatistics, Cengage Learning., 2004
Campbell, M.J; Statistics at Square Two: Understanding Modern Statistical Applications in Medicine, BMJ Books, 2001
Altman, D.; Practical Statistics for Medical Research, Chapman & Hall / CRC. , 1999
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