Course: 2024/2025

Biostatistics

(19760)

After completing the course, students should be able to:
- Understand the fundamental concepts of biostatistics.
- Demonstrate knowledge of study design principles and data analysis techniques in health sciences.
- Summarize data and represent them graphically.
- Comprehend probability theory and apply it to theoretical probability distributions.
- Estimate population parameters
- Conduct regression analysis and interprete the results.
- Perform hypothesis testing with qualitative and quantitative variables and interprete the results.
- Apply non-parametric statistics techniques.

Skills and learning outcomes

Description of contents: programme

1. Introduction. Basic concepts in biostatistics. Role of statistics in the research phases.
1.1. Statistical Terms: Populations, Individuals, and Samples
1.2. Types of Variables.
2. Introduction to study design in Health Sciences. Types of studies in biomedicine. Data analysis techniques.
2.1. Principles of Study Design
2.2. Types of Studies in Biomedicine: Experimental Studies, Observational Studies, Systematic Reviews, and Meta-Analysis
2.3. Techniques for Data Analysis in Biomedical Studies
3. Descriptive statistics. Sampling methods.
3.1. Descriptive Statistics:
3.1.1. Measures of Central Tendency and Dispersion
3.1.2. Graphical Representation of Data.
3.2. Sampling Methods: Simple Random Sampling, Stratified Sampling, Cluster Sampling and Systematic Sampling
4. Probability. Theoretical probability distributions.
4.1. Fundamentals of Probability Theory
4.1.1. Random Experiment
4.1.2. Development of concepts related to sample space, events, and their properties
4.1.3. Definition of probability and properties
4.1.4. Conditional Probability
4.1.5. Law of Total Probability and Bayes' Theorem
4.2. Discrete Random Variable
4.2.1. Probability Function
4.2.2. Cumulative Distribution Function and its properties
4.2.3. Common discrete distributions: Bernoulli, Binomial, Poisson
4.3. Continuous Random Variable
4.3.1. Cumulative Distribution Function and its properties
4.3.2. Probability Density Function
4.3.3. Common continuous distributions: Normal, Uniform, Exponential
5. Estimation of population statistical parameters. Regression analysis. Multivariate analysis.
5.1. Estimation of Population Parameters
5.1.1. Point Estimation
5.1.2. Confidence Intervals
5.2. Regression Analysis
5.2.1. Correlation Coefficient
5.2.2. Simple Linear Regression
5.2.3. Multiple Linear Regression
5.3. Introduction to Multivariate Analysis
6. Statistical theory of hypothesis testing. Hypothesis testing with qualitative variables. Hypothesis testing with quantitative variables.
6.1. Basic Concepts of Hypothesis Testing
6.2. Hypothesis Tests with Qualitative Variables
6.3. Hypothesis Tests with Quantitative Variables
6.4. Hypothesis Tests for Comparing Two Populations
7. Non-parametric statistics. Bayesian statistics.
7.1. Introduction to Non-Parametric Tests
7.2. Common Non-Parametric Tests: Mann-Whitney, Wilcoxon
7.3. Bayesian Statistics: Principles of Bayesian Inference

Learning activities and methodology

Classroom lectures.
Face-to-face classes: reduced.
Student individual work.
Final exam.
Lectures supported by computer and audiovisual aids.
Practical learning based on cases and problems, and exercise resolution.
Individual and group or cooperative work.
Individual tutoring for resolving doubts and inquiries about the subject.
Group reinforcement tutoring when necessary.

Assessment System

- % end-of-term-examination 40
- % of continuous assessment (assigments, laboratory, practicals...) 60

Calendar of Continuous assessment

Extraordinary call: regulations

Basic Bibliography

- Montgomery, D. C., & Runger, G. C.. Applied statistics and probability for engineers. John wiley & sons. 2010
- Yadav, S. K., Singh, S., & Gupta, R. . Biomedical Statistics. Springer Nature: Singapore. 2019

Additional Bibliography

- Bland, M. . An introduction to medical statistics. Oxford university press. 2015
- Rice, J. A. . Mathematical statistics and data analysis (Vol. 371). Belmont, CA: Thomson/Brooks/Cole. 2007

The course syllabus may change due academic events or other reasons.