Universitat Internacional de Catalunya
Biostatistics 2
Other languages of instruction: Catalan, English
Teaching staff
Adrián González Marrón (agonzalezm@uic.es)
Introduction
This subject is guided to train the students with the necessary biostatisic tools to critically assess the methodology of the research articles in Health Sciences, as well as to provide students with biostatistic techniques so they can develop and carry out research projects in Health Sciences.
In the area of Health Sciences, human populations are heterogeneous with respect to certain characteristics that may predispose a given disease or other outcomes in health. In this sense, the study of this variability with regression models has become a useful tool to study the relationship between disease and the characteristics of the population. The purpose of this subject is to present the regression models commonly used in research in Health Sciences.
Pre-course requirements
None
Objectives
- To present the most useful regression models depending on the purpose of the study and the variable of interest
- Design the classical regression models using statistical software
- To interpret the results of the regression models provided by the software
- To encourage the critical interpretation of scientific literature in which regression models are applied
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:
-
The student will be able to propose and justify a statistical analysis based on to the proposed study.
-
The student will be able to use a statistical program to run a Data analysis.
Syllabus
Block 1. Review Biostatistics 1
Block 2. Introduction to linear regression model
Block 3. Logistic regression model
Block 4. Introduction to survival analysis
- Kaplan-Meier and log-rank
- Cox regression model
- Advanced concepts in survival analysis
Teaching and learning activities
Online
|
Master classes: online adaptation (CT) (CP) |
|
Individual tutorials |
|
Group tutorials |
|
Autonomous learning: online adaptation (ML) |
|
Case Method: Online Adaptation (EC) |
|
Cooperative learning: online adaptation (RP) |
|
Project-based methodology: online adaptation (PBL) |
Evaluation systems and criteria
Online
|
Evaluation systems |
|||||
|
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.