Universitat Internacional de Catalunya
Biostatistics
Other languages of instruction: Catalan, English
Teaching staff
Teachers
Coordinador: Dr. Juan Carlos Martín (jcmartin@uic.es)
Introduction
This course is designed to train students with the tools needed to critically evaluate research articles published in scientific journals in the bioengineering field. As well as, provide students with tools to enable them to develop and carry out research projects.
The methodology used in this course consists of lectures, case methods and computer laboratory practices.
Pre-course requirements
No previous criteria are needed to study the subject.
Objectives
- Understand the concepts and basic statistical and epidemiological methods in health sciences.
- To enable the student to perform basic biostatistical techniques with a computer software specific for biostatistics.
- To train students for critical reading of scientific articles.
Competences/Learning outcomes of the degree programme
- CB3 - Students must have the ability to bring together and interpret significant data (normally within their area of study) and to issue judgements that include a reflection on important issues that are social, scientific or ethical in nature.
- CB4 - Students can transmit information, ideas, problems and solutions to specialist and non-specialist audiences.
- CB5 - Students have developed the necessary learning skills to undertake subsequent studies with a high degree of autonomy.
- CE1 - To solve the maths problems that arise in the field of Bioengineering. The ability to apply knowledge of geometry, calculate integrals, use numerical methods and achieve optimisation.
- CE3 - To apply fundamental knowledge on using and programming computers, operating systems, databases and IT programs to the field of Bioengineering.
- CG4 - To resolve problems based on initiative, be good at decision-making, creativity, critical reasoning and communication, as well as the transmission of knowledge, skills and prowess in the field of Bioengineering
- CT3 - To know how to communicate learning results to other people both verbally and in writing, and well as thought processes and decision-making; to participate in debates in each particular specialist areas.
- CT6 - To detect gaps in your own knowledge and overcome this through critical reflection and choosing better actions to broaden your knowledge.
- CT7 - To be fluent in a third language, usually English, with a suitable verbal and written level that is in line with graduate requirements.
Learning outcomes of the subject
- Understand the basic concepts of descriptive statistics. Knowing how to apply these appropriately according to the type of variable.
- Understand the concepts of probability and probability distribution
- Understand the concept of hypothesis testing, random and systematic error and statistical significance.
- Learn proper use basic hypothesis tests.
- Know how to interpret, both statistically and clinically, the results obtained in both descriptive and inferential statistics.
- Acquire skills for database management and statistical software to analyze data.
- Know how to present the numerical results in the context of an article and / or research project.
- Know how to perform a critical reading of the statistical results presented in scientific literature as original articles, review, ....
Syllabus
Unit 1. Introduction to statistics. Definition of statistics. Some basic concepts.
Unit 2. Probability. Concepts of probability: Bayes theorem. Probability functions. Density functions. The binomial distribution. The normal distribution. Standardization.
Unit 3. Descriptive statistics. Descriptive one-dimensional: tables of frequencies, statistical measures, graphical representation. Descriptive two-dimensional. Covariance, correlation, graphical representation and contingency tables.Unit 4. introduction to statistical inference. Sampling. Estimates point. Confidence intervals. Test of hypothesis: null and alternative hypothesis, type of error, and significance. Univariate inference.
Unit 5. Bivariate statistical inference. Comparison of proportions. Comparison of means. Correlation.
Unit 6. Statistical modelling. Linear regression. Logistic regression. Survival analysis.
Unit 7. Experimental design.
Teaching and learning activities
In blended
The subject will be taught blended through theoretical classes, problem solving sessions and computer classes.
The theory of the subject will be exposed in a rigorous way avoiding, however, an excess of formalization, which could mask the true purpose of the subject: teach the fundamentals of statistics to bioengineers. For this reason, conceptual clarity will be emphasized. In addition, students will learn to apply these concepts using statistical software.
Formation activities
- Master Class, resolution of exercises and computer classes: 60 h (Presence to decide)
- Preparation and performance of evaluable activities: 30 h (Presence: 0%)
- Autonomous study and exercise work: 60 h (Presence: 0%)
Evaluation systems and criteria
In blended
The assessment, with the pertinent adaptations to the blended modality, has three components:
1 Continuous assessment (25%) based on the presentation of oral and written works
2 Evaluation by an exam of the learning of the statistical software (30%)
3 Evaluation by a multiple choice theory test (45%).
To pass the course the student must obtain an average grade greater than 5 and a minimum score of 4.5 in each of the three components of the assessment of the subject.
Students who fail the course in the ordinary call will have an extraordinary announcement in July that will consist of a practical examination (30% of grade) and a multiple choice theory test (70% of grade). Students who have passed either party (practical and theoretical examination) will be retained to note the extraordinary announcement.
Important considerations:
- Plagiarism, copying or any other action that may be considered cheating will be zero in that evaluation section. Besides, plagiarism during exams will mean the immediate failing of the whole subject.
- In the second-sitting exams, the maximum grade students will be able to obtain is "Excellent" (grade with honors distinction will not be posible).
- Changes of the calendar, exam dates or the evaluation system will not be accepted.
- Exchange students (Erasmus and others) or repeaters will be subjected to the same conditions as the rest of the students.
Bibliography and resources
Bibliography
Martínez-González MA, Sánchez-Villegas A, Faulín Fajardo FJ. Bioestadística amigable (2ª ED). Díaz de Santos. Madrid; 2006. Peña D. Fundamentos en Estadística. 1 ed. Alianza Editorial, S.A.: Madrid; 2001. Sentís J, Pardell H, Alentà H, Cobo Valeri E, Canela i Soler J. Manual de bioestadística. 3a ed. Barcelona: Masson; 2003. Daniel W. Bioestadística: base para el análisis de las ciencias de la salud. 4a ed. México D.F.: Limusa; 2002. Cobo E, Muñoz P, González JA, Bogorra J. Bioestadística para no estadísticos: principios para interpretar un estudio científico. Barcelona: Elsevier Masson; 2007.Evaluation period
- E1 19/05/2021 A16 10:00h
- E2 22/06/2021 A19 10:00h