Biostatistics and introduction to research

The main objective of this subject is for students to become familiar with the basic concepts and techniques of Descriptive Statistics, Probability and Statistical Inference.
In addition, it is intended that students understand the need and utility of statistical methodology in research in Health Sciences, particularly in Dentistry, becoming aware of the scope and limitations of this methodology.
As more specific objectives, students must:
- Know the basic statistical language
- Know and apply some basic statistical methods to represent and analyse simple data sets, and be able to draw conclusions from those analysis.
- Know, express and correctly interpret the levels of precision, confidence and error levels in the conclusions of a statistical study.

Topic 1. Descriptive Statistics
Introduction to Statistics. Variables. Frequency distributions. Graphic representations. Position and dispersion measurements.
Topic 2. Probability
Random experiment. Sample space. Events. Conditioned probability. Independence of events. Product rule, law of total probabilities and Bayes' theorem. Sensitivity, specificity, positive and negative predictive values.
Topic 3. Random variables
Discrete and continuous random variables. Probability distributions: mass function, density function and distribution function. Measures of a random variable. The Binomial distribution and the Normal distribution. Approximation of the Binomial distribution by the Normal distribution.
Topic 4. Estimation and confidence intervals
Introduction to Statistical Inference. Parameter estimation. Confidence intervals for the proportion and for the mean and variance of a Normal population.
Topic 5. Hypothesis testing
Introduction. Null and alternative hypothesis. Types of errors in a hypothesis test. Level of significance and power of a test. Stages in the resolution of a hypothesis test. The critical level or p-value. Tests with a sample: test for the proportion, and for the mean and variance of a Normal population.
Topic 6. Association of categorical variables
Introduction. Contingency tables. Tests for categorical data: Chi-square test.
Topic 7. Regression models
Introduction to regression models. General concepts. The simple linear model. Parameter estimation: the least squares method. Inference about parameters. Decomposition of variability. Correlation coefficient and determination coefficient. Prediction.

BASIC
Borrajo, M. I. et al. (2020): “Estatística Descritiva”, Colección Esenciais USC. https://www.usc.gal/libros/gl/categorias/948-estatistica-descritiva-334…
Borrajo, M. I. et al. (2021): “Fundamentos da Teoría da Probabilidade”, Colección Esenciais USC. https://www.usc.gal/libros/gl/categorias/1025-fundamentos-da-teoria-da-…
Borrajo, M. I. et al. (2021): “O programa estatístico R”, Colección Esenciais USC. https://www.usc.gal/libros/gl/categorias/1024-o-programa-estatistico-r-…
Borrajo, M. I. et al. (2023): “Inferencia Estatística Paramétrica I”, Colección Esenciais USC. https://www.usc.gal/libros/gl/categorias/1183-inferencia-estatistica-pa…
Borrajo, M. I. et al. (2023): “Inferencia Estatística Paramétrica II”, Colección Esenciais USC. https://www.usc.gal/libros/gl/categorias/1182-inferencia-estatistica-pa…
García García, V. J. (2014): “Estadística descriptiva y Probabilidad”, Universidad de Cádiz. Available at https://prelo.usc.es/.
Kim, J.S. and Dailey, R. J. (2008): “Biostatistics for Oral Healthcare”, Blackwell.
Milton, J. S. (2007): “Estadística para Biología y Ciencias de la Salud”, McGraw-Hill-Interamericana.
Requena, F. (2008): “Introducción a la Estadística: Aplicación a la Odontología”, Proyecto Sur.
Rosner, B. (2011): “Fundamentals of Biostatistics”, Duxbury Press.
Verzani, J. (2005): “Using R for Introductory Statistics”, Chapman and Hall.

COMPLEMENTARY
Cao, R. et al. (1998): “Estadística básica aplicada”, Tórculo Edicións.
Daniel, W. W. (2002): “Bioestadística. Base para el análisis de las ciencias de la salud”, Limusa Wiley.
Martín Andrés, A. and Luna del Castillo, J. (2004): “Bioestadística para las ciencias de la salud”, Norma.
Pagano, M. and Gauvreau, K. (2001): “Fundamentos de Bioestadística”, Thomson-Learning.
Smeeton N. (2005): “Dental Statistics Made Easy”, Radcliffe Publishing (Oxford).

KNOWLEDGE
Con07.- Understand clinical and laboratory diagnostic procedures and tests, know their reliability and diagnostic validity, and be competent in interpreting their results.

COMPETENCIES
Comp01.- Ability to analyze and synthesize.
Comp02.- Ability to organize and plan.
Comp03.- Oral and written communication in the native language.
Comp05.- Basic computer skills.
Comp06.- Information management skills (ability to search for and analyze information from diverse sources).
Comp07.- Problem solving.
Comp08.- Decision making.
Comp09.- Critical and self-critical capacity.
Comp10.- Teamwork.
Comp13.- Ability to communicate with experts in other fields.
Comp14.- Appreciation of diversity and multiculturalism.
Comp16.- Ethical commitment.
Comp17.- Ability to apply knowledge in practice.
Comp18.- Research skills.
Comp19.- Autonomous learning ability.
Comp20.- Ability to adapt to new situations.
Comp21.- Ability to generate new ideas (creativity).
Comp24.- Ability to work autonomously.
Comp27.- Concern for quality.
Comp28.- Achievement motivation.

SKILLS OR ABILITIES
H/D08.- Knowing how to share information with other healthcare professionals and work in a team.
H/D09.- Understanding the importance of maintaining and using patient information records for later analysis, preserving data confidentiality.
H/D11.- Understanding the basic biomedical sciences on which Dentistry is based to ensure correct oral-dental care.
H/D18.- Knowing, critically evaluating, and knowing how to use clinical and biomedical information sources to obtain, organize, interpret, and communicate scientific and healthcare information.
H/D19.- Knowing the scientific method and having critical capacity to evaluate established knowledge and new information. Being able to formulate hypotheses, collect and critically evaluate information to solve problems, following the scientific method.

Lectures (30 hours): in the lectures, the teachers will explain the theoretical-practical concepts of the contents, relying on multimedia presentations. Some problems will also be solved, so that the students can work on the exercises that will be provided. Regarding the material for the monitoring of the subject, in spite of the recommended bibliography, the students will have complementary teaching material through the Virtual Campus of the subject.

Interactive teaching (26 hours): interactive teaching is distributed in exercises solving seminars and computer practices. In these sessions, students will be introduced to the R statistical software.

Tutorials: the tutorials are aimed at monitoring student learning. In these sessions, fundamental skills related to critical thinking and communication skills, among others, will be worked on.

The final grade for the subject will consist of 70% from the final exam (in the ordinary call or in the extraordinary call, if the subject is not passed in the first), and the remaining 30% from continuous assessment, distributed as follows:
- Exposition of exercises in the seminar sessions: 7.5%
- Practical exam on the R program: 7.5%
- Group work consisting of the statistical analysis of a database provided through the use of the statistical package R, and the preparation of the corresponding report: 15%
The practical exam on R and the project submission will take place during the teaching period, on dates that will be communicated in advance.

Partial recovery of continuous assessment
Students who do not pass the subject in the ordinary call may obtain (if they have not already done so) or improve their continuous assessment grade through:
- Submission of an individual project, with similar characteristics to the group project, based on a dataset provided by the teaching staff: 15%
These activities must be completed or submitted prior to the date of the final exam in the extraordinary call., within a deadline that will be established in advance. The grade obtained will replace the corresponding part of the continuous assessment. The part corresponding to the presentation of exercises in seminars (7.5%) is not recoverable, except in cases of justified absence. In such cases, the teaching staff may propose a compensatory alternative in accordance with the USC Attendance Regulations.

It should be noted that, for cases of fraudulent performance of exercises or tests, the provisions of the "Rules for the evaluation of student academic performance and review of grades" will apply.

In this matter, students have 56 hours of face-to-face teaching (30 hours of expository teaching and 26 hours of interactive teaching). For each hour of expository teaching, it is considered necessary to dedicate around 1.5 hours of student work to the study (review of concepts and bibliography consultation).
In relation to interactive teaching, for each hour one hour is considered necessary for class review. Additionally, students should bear in mind that it is necessary to practice problem solving.

Attendance to lectures and interactive sessions is essential for passing the subject. Students must carry out all the activities recommended by the teaching staff (problem solving, bibliography review and practical exercises) to successfully pass the subject.

Note that, in cases of fraudulent performance of exercises or tests, the provisions of the "Regulations for the evaluation of student academic performance and the review of grades" will apply.