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 - Describe the most prominent research methodologies and designs in the field of health
- 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.
M.2.5. Recognize the conceptual foundations, guidelines on measuring results, and the use of questionnaires or instruments in scientific evaluation in the health field.
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
First Call
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Oral Presentation: 15% of the final grade.
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Block Assignments: 85% of the final grade.
Block Structure: The course is divided into three blocks. Each block consists of two evaluative components:
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Short-answer questions: 25% of the block's grade.
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Statistical software assignment: 75% of the block's grade.
Final Assessment: At the end of the course, there will be an oral session. Students must present and justify the data analysis and results of a scientific paper within a research context.
Requirement: A minimum overall grade of 5 is required to pass the course.
Second CallIf the student does not pass the course during the first call, the grades for any passed components will be carried over. Only the failed components must be retaken.
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.