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Biomedical Statistics | LEB

Course:

Biomedical Enginnering

Curricular Unit (UC)

Biomedical Statistics

Mandatory  x
Optional  
Scientific Area MAT Category  

Course category: B - Basic; C - Core Engineering; E - Specialization; P - Complementary.

Year: 1st Semester: 2nd ECTS: 5.5 Total Hours: 
Contact Hours T: 30 TP: 30 PL: S: OT:3
Professor in charge

 Iola Maria Silvério Pinto

T - Lectures; TP - Theory and practice; PL - Lab Work; S - Seminar; OT - Tutorial Guidance.

  • Learning outcomes of the curricular unit

    After approval at UC, the student should be able to:

    Apply the techniques of descriptive statistics in the study of a set of data and interpret results

    Apply and recognize the concepts of probabilities in assessing situations of uncertainty, particularly in the analysis of diagnostic tests

    Identify the theoretical models in real situations

    Apply the techniques of statistical inference as a support tool for decision making and interpret the results

    Identify the use of the linear model and interpret the estimated coefficients

    Identify the use of the logistic regression model and interpret the estimated odds ratios

    Recognize the use of survival analysis models and interpret Hazard ratios

    Identify, plan and implement appropriate statistical methodology to analytical and computational problem solving using R (free) software

    Analyze, evaluate, interpret the results correctly

     

  • Syllabus

    Descriptive statistics: basic concepts, descriptive measures, graphical representations

    Theory of probabilities: conditional probability, Bayes theorem, diagnostic tests, discrete and continuous theoretical models

    Statistical inference: estimation, hypothesis testing

    Adjustment tests

    Tests for two samples: independent and paired

    Tests for more than two samples: independent and related

    Chi-square test and Fisher's exact test

    Linear regression model

    Logistic regression model

    Survival Analysis: basic concepts, Kaplan-Meier estimator, Cox regression model

  • Demonstration of the syllabus coherence with the curricular unit's objectives

    The syllabus contents are consistent with the goals of the curricular unit, given that:

    Point 1 of the syllabus aims to achieve the point 1 of the objectives;

    Point 2 of the syllabus aims to achieve points 2 and 3 of the goals;

    Points 3-7 of the syllabus intend to realize the point 4 of the objectives;

    Point 8 of the syllabus aims to achieve the point 5 of the objectives;

    Point 9 of the syllabus aims to achieve the point 6 of the objectives;

    Point 10 of the syllabus aims to achieve the point 7 of the objectives;

    The objectives referred to in points 8 and 9 are implemented throughout all items of the syllabus.

  • Teaching methodologies

    Classes are theoretical and theoretical-practical. Expository methodology is used for the presentation of theoretical matter, exemplifying with relevant problems within the Biomedical application. Then the student applies and consolidates the knowledge acquired in solving a set of problems in the context of this area of application. The computational implementation will be held in the software R (free).

    The knowledge assessment comprises two strands, continuous evaluation and assessment alternatives for examination, but compulsory in both the realization of a practical work individually or in a group.

    Continuous assessment is composed of two tests (with minimum of 8 values) carried out during the period of school. The assessment by examination is made up of the comprehensive examination.

  • Demonstration of the coherence between the teaching methodologies and the learning outcomes

    The teaching methodologies are consistent with the learning objectives, since expository methodology used to explain the theoretical concepts, specifically allows achieve all the learning objectives established for the unit. The exemplification with problems within the biomedical applications, enables students to understand how to apply the material to real situations. The  proposed problems are suitable for capacity building probabilistic and statistical reasoning. Beyond the analytical resolution, the use of the R software enables the student to acquire skills to solve real challenges.

    Given that the success in the course is not compatible with a timely study, it is useful to implement processes that contradict this trend. Mandatory completion of a practical work as well as the use of examples in biomedical applications, allow motivate students and provide them with a close contact with current challenges in this area of knowledge. Evaluation methods allow to ascertain whether the student has acquired sufficient knowledge to achieve the learning objectives proposed for the curricular unit.

  • Main Bibliography

    Agresti, A., “An Introduction to Categorical Data Analysis”, John Wiley & Sons, 3 nd Edition, 2014.

    Daniel, W. W., Cross, C. L.,” Biostatistics: A Foundation for Analysis in the Health Sciences”, 10th Edition, John Wiley & Sons, Inc., 2013.

    Daniel, W. W., Cross, C. L., “Biostatistics: A Foundation for Analysis in the Health Sciences”, 10th Student Solutions Manual , John Wiley & Sons, Inc., 2013.

    Pestana, D. D. e Velosa, S. F., “ Introdução à Probabilidade e à Estatística”, Volume I, 2ª Edição revista e actualizada. Fundação Calouste Gulbenkian , 3ª ed. revista e actualizada, 2008.

    Montgomery, D.C., Runger, G.C. “Applied Statistics and Probability for Engineers”, 6th edition, Wiley, 2014

    Venables, W., Smith, D. and the R Core Team. An Introduction to R. (http://cran.r-project.org/doc/manuals/r-release/R-intro.pdf), 2013