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José Alberto da Silva Freitas
Cristina Maria Nogueira da Costa Santos
Teresa Sarmento Henriques
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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”]
This unit aims to empower the students with theoretical foundations and practical approach to basic statistical methods used in clinical research, assessment of technologies and health service research. After this course unit the students should be able to: identify the correct statistical methodology for data analysis; apply these methods using statistical software; interpret the results of the statistical methods
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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, the students should be able to characterize different types of data and variables; computerize and process the data ; check for errors; describe graphically data; describe data with summary measures; apply basic techniques of statistical inferences ( point and interval estimation and hypothesis tests) and to apply a simple linear regression model. Students should also be able to criticize the statistical analysis of scientific articles published in the literature in Health Sciences area.
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[ms_accordion_item title=”Working method” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Presencial
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Handling data: types of data: data entry; error checking.
Descriptive statistics: measures of average and measures of spread; displaying data graphically.
Introduction to probability.
Normal distribution and other theoretical distributions. Statistical inference – Sampling and estimation.
Hypothesis testing: parametric tests – t test, F test (One-Way ANOVA);
Non-parametric tests – Mann-Whintey, Kruskal-Wallis, Wilcoxon and qui-square tests.
Statistical power and sample size calculation.
Correlation and simple linear regression.
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Lectures, group discussion and individual exercises. All the activities will be supported by an e-learning platform.
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[ms_accordion_item title=”Software” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
R Project for Statistical Computing
IBM Statistical Package for Social Sciences (SPSS)
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[ms_accordion_item title=”Evaluation Type” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Distributed evaluation with final exam
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[ms_accordion_item title=”Eligibility for exams” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
The frequency will be obtained according to the pedagogical regulation.
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[ms_accordion_item title=”Calculation formula of final grade” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
The final grade will be based on the result of a theoretical exam (50%) and componente based on practical application of the aquired knowledge (50%).
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[ms_accordion_item title=”Mandatory literature” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Aviva Petrie, Caroline Sabin; ; Medical Statistics at a Glance, Wiley-Blackwell, 2013. ISBN: ISBN: 978-0-470-65848-2
Campbell MJ, Swinscow TDV; tatistics at Square One, 11th Edition. BMJBooks, 2009
Bland JM; An Introduction to Medical Statistics, Oxford Medical Publications
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