Universitat Internacional de Catalunya
Biostatistics
Teaching staff
Teachers
Responsible: Dra. Cristina Lidón-Moyano (clidon@uic.es)
Ray Puig (raypuig@gmail.com)
Sonia de Paz Cantos (sdepaz@uic.es)
Introduction
This course is designed to train students with the tools needed to critically evaluate research articles published in medical journals. 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 theoretical presentations (30%) and case methods and computer laboratory practices (70%).
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 train students for critical reading of scientific articles.
Competences/Learning outcomes of the degree programme
general skills
- Teamwork and responsibility.
- Ability to adapt and decision making.
specific skills
- Acquisition of skills for critical reading of the scientific literature.
- Training students to formulate research hypotheses and evaluate scientific information.
- Basic Training to develop research projects and presentations of scientific results at conferences.
- 28 - Obtaining and using epidemiological data and assess trends and risks in health related decision-making.
- 31 - Understand, critically evaluate and know how to use sources of clinical and biomedical information to obtain, organize, interpret and communicate scientific and health care information.
- 33 - Maintain and use records with patient data for later analysis, preserving the confidentiality of the data.
- 34 - Ability for critical thinking, creativity and constructive skeptisim with a focus on research within professional practice.
- 35 - Understand the importance and limitations of scientific thinking in the study, prevention and treatment of disease.
- 36 - Be able to formulate hypotheses, collect and critically evaluate information for problem solving using the scientific method.
- 37 - Acquire basic training for research.
- CB-1 - To have acquired advanced knowledge and demonstrated, within the context of highly specialised scientific and technological research, detailed comprehension based on theoretical and practical aspects and a working methodology from one or more fields of study.
- CB-2 - To know how to apply and incorporate knowledge, an understanding of it and its scientific basis and the ability to solve problems in new and loosely defined environments, including multidisciplinary contexts that include both researchers and highly specialised professionals.
- CB-3 - To know how to evaluate and select the appropriate scientific theories and precise methodologies required by their field of study to make judgements based on incomplete or limited information. Where necessary and appropriate, this includes a reflection on the ethical and social responsibility linked to the solution suggested in each case.
- CB-4 - To be able to predict and control the evolution of complex situations through the development of new and innovative working methodologies adapted to the scientific / research, technological or specific professional field, which is generally multidisciplinary, within which they undertake their activities.
- CB-6 - To have developed sufficient autonomy to participate in research projects and scientific or technological cooperation within the student’s own thematic and interdisciplinary context. This should also include a high degree of knowledge transfer.
- CTP-3 - To develop critical thinking and reasoning as well as self-assessment skills.
Learning outcomes of the subject
It is expected that students acquire the following learning outcomes:
- 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.
- 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
Block 1: Introduction to research
Introduction to research
Research in Medicine and Health Sciences
Statistical method
Type and description of variables
Block 2: Descriptive statistics
One-dimensional descriptive statistics:
Frequency distribution
Centralization measures
Dispersion measurements
Position measurements
Graphic representation
Shape measurements
Two-dimensional descriptive statistics:
Joint frequency distribution
Marginal distribution
Conditional distribution
Dependency or association measures
Regression line
Block 3: Statistical Inference
Sample designs
Random variables and sample distribution
Statistical inference (I):
Introduction to inference
One-dimensional statistical inference (II):
Punctual estimation
Confidence intervals
Hypothesis contrasts
Introduction to hypothesis testing
Two-dimensional statistical inference:
Difference of two proportions
Difference of two stockings
Correlation and regression line
Teaching and learning activities
In person
| TRAINING ACTIVITY | METHODOLOGY | HORAS ALUMNO |
|---|---|---|
| MASTER LESSON | TEACHER'S LESSON TEACHER'S PRACTICE | 24 |
| PROBLEM BASED LEARNING | SELF LEARNING TEACHER'S PRACTICE | 6 |
| CASE METHOD | SELF LEARNING TEACHER'S PRACTICE | 8 |
| LABORATORY PRACTICE | SELF LEARNING TEACHER'S PRACTICE | 24 |
| VIRTUAL KNOWLEDGE | SELF LEARNING ON LINE | 10 |
Evaluation systems and criteria
In person
First call
• Continuous evaluation (30%): Works of the different methods of the case
• Final theoretical exam (70%)
(10% extra): Voluntary activities
* exercises and exams will generally be deliverated via moodle.
* Minimum grade of 5 in the exam to average
*80% compulsory assistance to practices
Second call
• Theoretical exam (100%)
* Minimum grade of 5 in the exam
Bibliography and resources
OpenIntro Statistics. Third Edition. David M Diez, Christopher D Barr, and Mine Çetinkaya-Rundel.
Estadística aplicada a las ciencias de la salud. Joaquín Moncho Vasallo.
Estadística para biología y ciencias de la salud. J. Susan Milton.
Evaluation period
- E1 17/01/2025 I2 10:30h
- E1 17/01/2025 I1 10:30h
- E1 17/01/2025 I3 10:30h
- R1 29/01/2025 A21 11:00h