Universitat Internacional de Catalunya
Biostatistics 1
Other languages of instruction: English, Spanish
Teaching staff
Adrián González
agonzalezm@uic.es
Introduction
In Health Sciences, researchers study phenomena affected by variability. Clinical observation and the search for responses create the necessity to measure, order and systematize data to subsequently apply laws and extrapolate the obtained results to the study population, thus drawing conclusions for better clinical practice. Biostatistics is the science, that with scientific rigor, gives us the quantitative tools to reach this goal. Besides, it is necessary to perform analyses and critical readings of scientific literature and to promote clinical practices based on scientific evidence.
Pre-course requirements
Not needed
Objectives
- To know the most frequent basic statistical concepts and methods in Health Sciences and their applications.
- To qualify the student to carry out the basic biostatistical analyses with statistical software.
- To qualify the student to critically appraise scientific articles.
Competences/Learning outcomes of the degree programme
- CN02RA - Describir las metodologías y diseños de investigación más destacados en el ámbito de la salud.
- HB01RA - Apply the scientific method, research design, advanced biostatistics and quantitative and qualitative data analysis tools to solve a question or test a hypothesis in the clinical setting.
Learning outcomes of the subject
The student will be able to:
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The student will be able to propose and justify a statistical analysis based on to the proposed study.
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The student will be able to use a statistical program to run a Data analysis.
Syllabus
Unit 1. Introduction to biostatistics
- Types of variables
- Sample and population
Unit 2. Descriptive statistics
Unidimensional descriptive statistics
- Frequency tables
- Measures of central tendency
- Measures of dispersion
- Measures of position
- Measures of shape
Graphical representation of data
Bidimensional descriptive statistics
- Contingency tables
- Correlation and introduction to regression
Block 3. Statistical inference
- Point estimate and confidence intervals
- Hypothesis testing
- Inference for one variable
- Inference for two variables
Teaching and learning activities
Online
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Master classes: online adaptation (CT) (CP) |
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Individual tutorials |
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Group tutorials |
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Autonomous learning: online adaptation (ML) |
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Case Method: Online Adaptation (EC) |
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Cooperative learning: online adaptation (RP) |
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Project-based methodology: online adaptation (PBL) |
Evaluation systems and criteria
Online
|
Evaluation systems |
|||||
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1 |
Continuous assessment: online adaptation (GP) |
Minimum weighting |
10% |
Maximum weighting |
20% |
|
2 |
Written work: online adaptation (PT) |
Minimum weighting |
40% |
Maximum weighting |
60% |
|
3 |
Oral presentation: online adaptation (GP) |
Minimum weighting |
10% |
Maximum weighting |
20% |
|
4 |
Written tests: online adaptation (OM) (PA) |
Minimum weighting |
25% |
Maximum weighting |
40% |
- Participation in forum discussions (5%)
- Quiz (5%)
- Exercise of short questions (15%)
- Work with statistical software (60%)
- Oral presentation (15%)
For each of the three blocks of the course there will be three evaluation elements: test, short questions exercise and exercise with statistical program. In addition, during the course, a debate will be opened in the forum, in which the students' participation will be evaluated. Finally, at the end of the course, there will be an oral session in which students will have to present and justify the analysis of data and the presentation of results of a scientific article, in the context of research.
It is mandatory to complete the three evaluative elements of each of the blocks, as well as the oral presentation, in order to pass the course.
In case of not passing the course in the first call, the grade of the approved elements will be maintained for the second call, having to recover those not approved, except for the participation in forum debates.
Bibliography and resources
Martínez-González MA, Sánchez-Villegas A, Faulín Fajardo FJ. Bioestadística amigable (4ª ED). Díaz de Santos. Madrid; 2020.
Piédrola Gil, et al. Medicina Preventiva y Salud Pública. 12ª Edición. Barcelona: Masson S.A.; 2015.
Pardo A, Ruiz MA. Análisis de datos en ciencias sociales y de la salud (vols. I y II) (1ª Ed). Editorial Síntesis. 2012.