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Course unit
STATISTICAL METHODS FOR CLINICAL RESEARCH
PSO2043915, A.A. 2015/16
Information concerning the students who enrolled in A.Y. 2015/16
ECTS: details
Type |
Scientific-Disciplinary Sector |
Credits allocated |
Educational activities in elective or integrative disciplines |
SECS-S/01 |
Statistics |
6.0 |
Course unit organization
Period |
First semester |
Year |
1st Year |
Teaching method |
frontal |
Type of hours |
Credits |
Teaching hours |
Hours of Individual study |
Shifts |
Lecture |
6.0 |
42 |
108.0 |
No turn |
Examination board
Board |
From |
To |
Members of the board |
2 2017-1 |
01/10/2017 |
30/09/2018 |
CAPIZZI
GIOVANNA
(Presidente)
PASTORE
MASSIMILIANO
(Membro Effettivo)
TARANTINO
VINCENZA
(Membro Effettivo)
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1 2017 |
01/10/2016 |
30/09/2017 |
CAPIZZI
GIOVANNA
(Presidente)
PASTORE
MASSIMILIANO
(Membro Effettivo)
TARANTINO
VINCENZA
(Membro Effettivo)
|
Prerequisites:
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Students need to have elementary knowledge of probability and basic statistics. |
Target skills and knowledge:
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Introduction of the main statistical tools, univariate and multivariate, for biomedical applications. Students will develop skills to analyze, using a statistical software, the relationship between a specific bio-medical/psychological status and several predictors. |
Examination methods:
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Written examination. Multiple choice questions concerning the statistical analysis of real data. Reports and analysis are performed in the lab using the R software. |
Assessment criteria:
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The evaluation of the preparation of students will be based on the understanding of the handled topics, the acquisition of concepts and skills to apply them. |
Course unit contents:
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Univariate and multivariate explorative statistical analysis of collected data. Statistical tools for testing association and
dependence among categorial and continuous experimental data in the biomedical framework: model free techniques for multiway contingency tables. Generalized Linear Models (linear and logistic multiple regression). ANOVA for independent and repeated measures. Introduction to non-parametric statistics. Tree-based procedures. |
Planned learning activities and teaching methods:
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Lectures.
Labs are the core of the course. Case studies are analyzed using the R language. Practical problems are discussed doing an accurate exploratory data analysis and fitting suitable univariate and multivariate statistical models. |
Additional notes about suggested reading:
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Slides of the lectures and written comments to the case studies, discussed during labs, will be available on the website. |
Textbooks (and optional supplementary readings) |
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Triola M. M., Triola M. F., “Statistica per le discipline biosanitarie“. --: Pearson Education It, 2009. Cap. 1-8, 10, 12
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Fox J., Applied regression analysis, linear models, and related methods. --: Sage, 1997. Cap. 5-15
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