Universitat Internacional de Catalunya - Barcelona

Biostatistitcs.

• code 11435
• course 1
• term Anual
• type OB
• credits 1

Main language of instruction: English

Other languages of instruction: Spanish

Head instructor

Dr. Josep Maria HUGUET - jmhuguet@uic.es

Office hours

The student should contact by email to arrange a meeting:

Dr. Josep Maria Huguet (jmhuguet@uic.es) - Coordinator & Professor

Biostatistics is a fundamental discipline in Health Sciences and its knowledge is basic to conduct research and critically appraise scientific literature. It validates research, and it is crucial to understand it and use it.

Basic knowledge of mathematics.

To know the basic statistical concepts and their applications in Health Sciences.

To train students to perform basic statistical analysis using a statistical software.

To train students to perform critical reading of scientific articles.

• CB10 - Acquire learning skills that allow them to continue studying in a self-directed and autonomous mode.
• CB6 - Knowledge and understanding to provide a basis or opportunity for originality in developing and / or applying ideas in a research context.
• CB7 - To apply the acquired knowledge, and develop their ability to solve problems in new environments within broader (or multidisciplinary) contexts related to their field of study.
• CB8 - Integrate knowledge and deals with the complexity of formulate judgments based on scientific evidence, from information that may be incomplete or limited, include reflecting on social and ethical responsibilities linked to the application of their knowledge and judgments.
• CE1 - Ability to apply the scientific method, the experimental design and the biostatistics to solve a problem or corroborate hypothesis.
• CE3 - Ability to apply bioinformatics tools used in basic research.
• CG1 - Ability to integrate new knowledge through research and study, and deal with complexity
• CG2 - Ability to review analysis and discussion of the experimental results and to issue the corresponding conclusions.
• CT1 - Ability to work in multidisciplinary and multicultural groups

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.
• Acquire skills for database management and statistical software to analyze data.
• 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, ....

1. Introduction to Statistics. Definition. Concepts. Biostatistics and Health Science.
2. Sampling. Observational vs. Experimental data. Theory of sampling.
3. Descriptive Statistics. Definition. Frequency distributions. Graphical reporting. Measures of Statistics.
4. Statistical Inference and the Normal distribution. Definition. Probability distributions. Properties of the Normal distribtution. The Standard Normal distribution. Area under the curve. The Central Limit Theorem.
5. Using Normal Distributions. Tests for Normality. Proportions. Mean. Confidence intervals. Sample size.
6. Hypothesis testing. Definition. Concepts involved. Examples. Technique. Overview of tests. The Student’s t-distribution. Importance in hypothesis testing. Use of tables. Non-parametric tests.
7. Comparing two populations (I). Introduction. Types of comparison tests. Equal variance Student’s t-test. Welch’s Unequal variance t-test. Paired Student’s t-test. Proportions test.
8. Comparing two populations (II). Mechanism of comparsison. Sample size when comparing. The χ2 distribution. McNemar test.
9. Relationship. Definition. Relationship between two continuous variables. Relationship between two dichotomous variables.
10. Advanced techniques. Logistic regression, ANOVA and non-parametric statistics.

1. Altman DG. "Practical statistics for medical research". Londres: Chapman and Hall/CR. 1999.
2. Martín Andrés A, Luna Del Castillo J. "Bioestadística para las Ciencias de la Salud". Madrid: Capitel Ediciones, S.L., 2004
3. Milton JS. "Estadística para Biología y Ciencias de la Salud”, 3ª Ed. Madrid: Interamericana McGraw-Hill, 2001.
4. Motulsky H. "Intuitive Biostatistics". N. York: Oxford University Press, 1995.
5. http://estadisticaorquestainstrumento.wordpress.com
6. Martínez-González MA, Sánchez-Villegas A, Faulín Fajardo FJ. Bioestadística amigable (2ª ED). Díaz de Santos. Madrid; 2006.
7. Gordis L. Epidemiology. 3a edición. Ediciones Hancourt S.A., 2005.
8. Szklo M, Nieto J. Epidemiología intermedia: Conceptos y Aplicaciones. Díaz de Santos; 2003.
9. Argimon Pallás JM, Jiménez Villa J. Métodos de investigación clínica y epidemiológica. 3 ed. Elsevier España S.A.: Madrid; 2004.