Universitat Internacional de Catalunya

Biostatistics 2

Biostatistics 2
3
12186
1
First semester
OB
Main language of instruction: Spanish

Other languages of instruction: Catalan, English

Teaching staff


Head instructor: Adrián González Marrón (agonzalezm@uic.es)

Introduction

This subject is guided to train the students with the necessary biostatisic tools to critically assess the methodology of the research articles in Health Sciences, as well as to provide students with biostatistic techniques so they can develop and carry out research projects in Health Sciences.

In the area of Health Sciences, human populations are heterogeneous with respect to certain characteristics that may predispose a given disease or other outcomes in health. In this sense, the study of this variability with regression models has become a useful tool to study the relationship between disease and the characteristics of the population. The purpose of this subject is to present the regression models commonly used in research in Health Sciences.

Pre-course requirements

None

Objectives

  1. To present the most useful regression models depending on the purpose of the study and the variable of interest
  2. Design and validate the classical regression models
  3. To interpret the results of the regression models provided by the software
  4. To encourage the critical interpretation of scientific literature in which regression models are applied

Competences/Learning outcomes of the degree programme

  • CN01M - Identify health problems in the field of emergencies and health emergencies that can be investigated.
  • CN04M - Identify the quantitative and qualitative tools and methods necessary for the design and development of a research study in the clinical field of emergencies.
  • CP02M - Communicate clinical findings, health outcomes and/or research project in new, specialized and non-specialized settings clearly and unambiguously.
  • HB03M - Demostrar iniciativa y proactividad por parte del alumnado de manera autònoma y autodirigida en el proceso de aprendizaje mediante la capacidad de investigar, analizar información, sintetizar conocimientos y resolver problemas
  • HB05M - Demonstrate the use of tools related to information and communication technologies and their application to specific fields of knowledge

Learning outcomes of the subject

Students will be able to determine the statistical methods needed to answer scientific questions arising when conducting a research study. They will learn to formulate statistical hypotheses from a scientific question and answer from a statistical standpoint. You will learn to make models of multiple linear regression, logistic regression and Cox regression, and selecting the regression model according to the purpose of the study and the data available.

The students will develop skills to read critically the statistical methodology and the results of a scientific article and they will also be able to interpret and communicate the results of the regression models. Additionally, the student will be familiar with the use of statistical software that allows you to perform the analysis of the data generated in your research, knowing estimate, form and validate regression models using such statistical software.


 At the end of the course, the student will be able to:

  •  Apply the scientific method, research design, advanced biostatistics, and quantitative and qualitative data analysis tools to solve a question or test a hypothesis in the clinical setting.
  • Design an advanced research project on clinical aspects following ethical standards and scientific rigour and respecting the fundamental rights of equality between men and women, and the promotion of human rights and the values of a culture of peace and democratic values, using language that avoids androcentrism and stereotypes.
  • Describe the most outstanding research methodologies and designs in the field of health.
  • Use tools to critically evaluate projects, protocols and research articles, both qualitative and quantitative, in the field of health sciences.
  • Identify health problems that can be investigated.

Syllabus

Block 1. Review Biostatistics 1

Block 2. Introduction to linear regression model and ANOVA

Block 3. Logistic regression model

  • Epidemiology review: Measures of frequency and measures of association

Block 4. Introduction to survival analysis

  • Kaplan-Meier and log-rank
  • Cox regression model
  • Advanced concepts in survival analysis

Teaching and learning activities

Online



Master classes: online adaptation (CT) (CP)

Individual tutorials

Group tutorials

Autonomous learning: online adaptation (ML)

Case Method: Online Adaptation (EC)

Cooperative learning: online adaptation (RP)

Project-based methodology: online adaptation (PBL)

Evaluation systems and criteria

Online



Evaluation systems

1

Continuous assessment: online adaptation (GP)

Minimum weighting

10%

Maximum weighting

20%

2

Written work: online adaptation (PT)

Minimum weighting

40%

Maximum weighting

60%

3

Oral presentation: online adaptation (GP)

Minimum weighting

10%

Maximum weighting

20%

4

Written tests: online adaptation (OM) (PA)

Minimum weighting

25%

Maximum weighting

40%

Bibliography and resources

Martínez-González MA, Sánchez-Villegas A, Faulín Fajardo FJ. Bioestadística amigable (2ª ED). Díaz de Santos. Madrid; 2006.


Pardo Merino A, Ruiz Diaz MA, San Martín R. Análisis de datos en Ciencias Sociales y de la Salud (2ª ED). Editorial Síntesis.