ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Student's work ECTS: 91 Hours of tutorials: 3 Expository Class: 36 Interactive Classroom: 20 Total: 150
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Faculty of Medicine and Dentistry
Call: First Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The main objective of this course is to acquaint students with the basic concepts and techniques of Descriptive Statistics, Probability, and Statistical Inference.
In addition, students are intended to understand the need and usefulness of statistical methodology in Health Sciences research, particularly in Medicine, becoming aware of the scope and limitations of such methodologies.
Regarding more specific contents the students have:
- to know the basic statistical language: to provide students with the basic theoretical knowledge that will allow them to understand the different statistical and probabilistic aspects involved in medical/health research;
- to know and apply some basic statistical methods to represent and analyse simple datasets, and be able to draw conclusions from such analyses;
- to know, express and correctly interpret levels of precision, confidence, and levels of error in the conclusions of an statistical study.
Chapter 1.- Definition and objectives of Statistics. Statistics in medical research. Study design, population, and sample. Types of data. Presentation of data: frequency tables. Measures of centralization, position, dispersion, and shape. Graphical representations: bar and sector diagrams, histograms, and boxplots.
Chapter 2.- Random experiment. Sample space. Events. Axiomatic definition of probability. Conditional probability. Independent events. Bayes' rule. Diagnostic tests: sensitivity and specificity. Prevalence and incidence. Measures of effect: Relative Risk (RR) and Odds Ratio (OR).
Chapter 3.- Probability distributions of discrete variables. Probability mass function. Distribution function. Expected value. Variance. Binomial distribution. Poisson distribution.
Chapter 4.- Probability distributions of continuous variables. Density function. Distribution function. Expectation. Variance. The Normal distribution. Probability or normality intervals. Approximation of a Binomial variable to the Normal. Cut-off points for the diagnosis of diseases.
Chapter 5.- Parameter. Statistics. Distribution of different statistics. Central Limit Theorem. Distributions associated with the Normal: Chi-Square, T-Student.
Chapter 6.- Statistical Inference. Point estimation and interval estimation: definitions and properties of point estimators. Estimation of means and variances in Normal populations and proportions.
Chapter 7.- Introduction to hypothesis tests. Null and alternative hypotheses. Types of contrasts. Regions of acceptance and rejection. Type I error, level of significance. Type II error, power of a contrast.
Chapter 8.- One- sample tests: for the mean and for a proportion. Two-sample tests: comparison of two means (independent samples, paired samples).
Chapter 9.- Tests for categorical data: Contingency tables. Chi-square test.
Chapter 10.- Introduction to regression methods. General concepts. Least Squares method. Correlation: linear correlation coefficient. Variance decomposition in regression.
- Álvarez Cáceres, R. (2007) “Estadística Aplicada a las Ciencias de la Salud”. Editorial Diaz de Santos.
- Daniel, W.W. (2006) “Bioestadística. Base para el análisis de las ciencias de la salud”. (2ª ed). Editorial LIMUSA. Wiley.
- Douglas G. A. (1997) “Practical Statistics for Medical Research”. Ed. Chapman & Hall.
- Martín Andrés, A. y Luna del Castillo, J. (1994) “Bioestadística para las ciencias de la salud”. (4ª ed). Ediciones Norma.
- Martín Andrés, A. y Luna del Castillo, J. (1995) “50 +/- 10 horas de Bioestadística”. Ediciones Norma.
- Martínez González, M.A; Sánchez, A. y Faulin, J. (2006). “Bioestadística amigable”. 2ª ed. Editorial Diaz de Santos.
- Milton, J.S. (1994) “Estadística para biología y ciencias de la salud”. (2ª ed). Ed. Interamericana, McGraw-Hill.
- Paradis, E. (2003). R para principiantes. R Cran. Available at https://cran.r-project.org/doc/contrib/rdebuts_es.pdf
- Quesada, V. y otros (1982) “Curso de ejercicios de estadística”. (2ª ed). Editorial Alambra.
- Rosner, B. (2000) “Fundamentals of Biostatistics”. (5ª ed). Wadsworth Publishing Company. Duxbury Press.
- Venables, W.N., Smith, D.M. and the R Core Team (2020). An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics (Version 3.6.3). Available at https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.
- Verzani, J. (2005). Using R for Introductory Statistics. Chapman and Hall.
Within this subject, we will try to contribute to the students' achievement of competences included in the report for the Degree in Medicine at the USC:
CG28.- Obtain and use epidemiological data and assess trends and risks for health decision-making.
CG31.- To know, critically evaluate and know how to use clinical and biomedical information sources to obtain, organize, interpret, and communicate scientific and health information.
CG33.- Maintain and use records with patient information for later analysis, preserving the confidentiality of the data.
CG34.- To have, in the professional activity, a critical and creative point of view, with constructive and research-oriented scepticism.
CG35.- Understand the importance and limitations of scientific thinking in the study, prevention, and management of diseases.
CG36.- Be able to formulate hypotheses, collect, and critically evaluate information for problem solving following the scientific method.
GG37.- Acquire basic training for research activity.
CEMII.32.- To know the basic concepts of Biostatistics and its application to the medical sciences.
CEMII.33.- Be able to design and carry out simple statistical studies using computer programs and interpret the obtained results.
CEMII.34.- Understanding and interpreting statistical data in the medical literature.
The lectures and interactive teaching will be face-to-face, according to the distribution agreed by the Faculty of Medicine, and will be complemented by the Virtual Campus of the subject, where students will find bibliographic materials, problem sheets, explanatory videos, etc. Through the Virtual Campus, students will also be able to take tests and submit assignments for continuous assessment, as described in the corresponding section.
Lectures (36 hours): in the expository teaching sessions, the teaching staff will explain theoretical and practical concepts of the contents, supported by multimedia presentations. Some standard problems will also be solved, so that students can work on the exercise reports that will be provided. With regard to the material for monitoring the course in addition to recommended bibliography, students will have complementary teaching material available through the course's Virtual Campus. The general competences CG.07, CG.18, CG.19 as well as the specific CEMII.32 and CEMII.34 will be worked on in the expository teaching sessions.
Interactive teaching (24 hours): interactive teaching is distributed in seminars for solving classroom exercises and computer practices. In these sessions, students will be introduced to the use of the R package for statistical data analysis. They will also have to work and solve practical cases. To follow up the computer practice sessions, students will be provided with practice scripts. In the interactive teaching sessions, the following skills will be enhanced: CG.28, CG.31, CG.33, CG.34, CG.36, CEMII.33 and CEMII.34.
Tutorials: the tutorials are intended to follow up the students' learning. They will be carried out both in person and through e-mail or MS Teams. In these sessions we will work mainly on those competences related to critical reasoning and communication skills.
Continuous evaluation (25%): continuous evaluation will be based on participation in two types of tasks: those associated with interactive seminar sessions (individual or group problem solving) and those associated with interactive laboratory sessions (resolution of practical exercises with R, the statistical suite). These activities can be carried out both in the classroom and in the field.
Final exam (75%): the final exam will consist of several theoretical and practical questions and problems about the contents of the subject, within which the interpretation of results obtained with the statistical package used in interactive teaching may be included.
The weight of the continuous evaluation in the recovery opportunity will be the same as in the ordinary call of the semester.
It should be noted that in cases of fraudulent exercises or tests, the provisions of the "Regulations for the Evaluation of Students' Academic Performance and for the Review of Grades" will apply.
In this course, students have 60 hours of classroom teaching (36 hours of lectures and 24 hours of interactive teaching). For each hour of lectures, it is considered necessary to dedicate around 1.5 hours of student’s work to the study (for concept review and consultation of bibliography).
In relation to interactive teaching, one hour is considered necessary for class prerataion and revision. In addition, students should keep in mind that it is necessary to make practice solving problems (either from bulletins or from the recommended bibliography).
It is recommended that students follow the lectures and interactive sessions, as well as the proposed activities, as a fundamental means of making the most of the subject.
In order to successfully pass the subject, it is also advisable to follow the proposed work plans. It is also recommended that the student practices the use of the statistical package R to explore the possibilities of the different techniques explained throughout the course.
The course material will be available to students through the USC Virtual Campus. We intend that this platform will be the main means of communication with students, reinforced with MS Teams and email.
Contingency plan:
SCENARIO 2 (distancing)
Teaching methodology: Partially virtual teaching in a synchronous manner, in accordance with the distribution organised by the Faculty of Medicine. The Virtual Campus of the course will be used, with materials provided by the teaching staff, as well as the MS Teams platform. Tutorials will be attended by email or through MS Teams.
Assessment system: Continuous assessment will consist of problem solving, individually, and the completion of practical tasks using the statistical tool R, which may be individual, both face-to-face and online. The final exam will be face-to-face whenever health conditions allow it. The weight of the continuous assessment and the final exam in the final mark of the subject will be the same as described in the section "Assessment System" of this guide, both in the first and second opportunity.
SCENARIO 3 (closure of the facilities)
Teaching methodology: Totally remote teaching supported by the virtual course platform and the MS Teams tool, with the substitution of some face-to-face activities by asynchronous material. Tutorials by email or MS Teams.
Assessment system: Continuous assessment will consist of problem solving, on an individual basis, and the completion of practical tasks using the statistical tool R. The face-to-face activities of scenarios 1 and 2 will be carried out in a non-face-to-face manner using the corporate tool MS Teams. The weight of the continuous assessment and the final exam in the final grade of the subject will be the same as described in the section "Assessment System" of this guide, both in first and second opportunity.
This guide and the criteria and methodologies described in it are subject to modifications derived from the regulations and guidelines of the USC.
Carmen Maria Cadarso Suarez
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881812282
- Category
- Professor: University Professor
Maria Isabel Borrajo Garcia
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariaisabel.borrajo [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Jose Ameijeiras Alonso
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813165
- jose.ameijeiras [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
Alejandro Saavedra Nieves
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- alejandro.saavedra.nieves [at] usc.es
- Category
- Professor: Temporary supply professor to reduce teaching hours
Maria Alonso Pena
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- mariaalonso.pena [at] usc.es
- Category
- Xunta Pre-doctoral Contract
| Monday | |||
|---|---|---|---|
| 08:30-09:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 10:30-11:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 15:30-16:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 17:30-18:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Spanish | Medicine-Classroom 4 |
| Tuesday | |||
| 08:30-09:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 10:30-11:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 15:30-16:30 | Grupo /CLE_03 | Spanish | Medicine-Classroom 4 |
| 17:30-18:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Spanish | Medicine-Classroom 4 |
| 19:30-20:30 | Grupo /CLIS_08 | Galician | Medicine-Classroom 2 |
| Wednesday | |||
| 08:30-09:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 10:30-11:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 15:30-16:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 17:30-18:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Spanish | Medicine-Classroom 4 |
| Thursday | |||
| 08:30-09:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 10:30-11:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 15:30-16:30 | Grupo /CLE_03 | Spanish | Medicine-Classroom 4 |
| 17:30-18:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Spanish | Medicine-Classroom 4 |
| Friday | |||
| 08:30-09:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 10:30-11:30 | Grupo /CLE_02 | Galician | Medicine-Classroom 5 |
| 11:30-12:30 | Grupo /CLE_01 | Galician | Medicine-Classroom 4 |
| 15:30-16:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 17:30-18:30 | Grupo /CLE_04 | Galician | Medicine-Classroom 5 |
| 18:30-19:30 | Grupo /CLE_03 | Spanish | Medicine-Classroom 4 |
| 12.18.2021 09:00-12:00 | Grupo /CLE_01 | Medicine-Classroom 3 |
| 12.18.2021 09:00-12:00 | Grupo /CLE_01 | Medicine-Classroom 4 |
| 12.18.2021 09:00-12:00 | Grupo /CLE_01 | Medicine-Classroom 5 |
| 12.18.2021 09:00-12:00 | Grupo /CLE_01 | Medicine-Classroom 6 |
| 12.18.2021 09:00-12:00 | Grupo /CLE_01 | Medicine-Classroom 7 |
| 12.18.2021 09:00-12:00 | Grupo /CLE_01 | Medicine-Classroom 8 |
| 07.08.2022 16:30-18:30 | Grupo /CLE_01 | Medicine-Classroom 4 |
| 07.08.2022 16:30-16:30 | Grupo /CLE_01 | Medicine-Classroom 5 |
| 07.08.2022 16:30-18:30 | Grupo /CLE_01 | Medicine-Classroom 7 |
| 07.08.2022 16:30-18:30 | Grupo /CLE_01 | Medicine-Classroom 8 |