Checking date: 02/06/2021


Course: 2021/2022

Biostatistics
(17766)
Study: Master in Statistics for Data Science (345)
EPI


Coordinating teacher: DURBAN REGUERA, MARIA LUZ

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Probability Statistical Inference Programming in R Regression Models
Objectives
CG1 Ability to apply analysis techniques with the aim to adapt the information to real problems. CG2 Ability to identify the best stochastic model for each real problem, and to apply it for its analysis, design and solution. CE5 Apply advanced statistical foundation for the development and analysis of real problems that include the prediction of a response variable. KNOWLEDGE ACQUISITION 1) Clinical Trials 2) Survival analysis 3) Models for longitudinal data and repeated measurements
Skills and learning outcomes
Description of contents: programme
1 Clinical Trials Data Analysis 1.1 Basic concepts 1.2 Treatment comparisons 1.3 Meta-analysis 2 Survival analysis 2.1 Basic concepts 2.2 Descriptive methods for survival data 2.3 Regression models for survival data 3 Models for longitudinal data and repeated mesurements 3.1 Hierarchical data 3.2 Models with random intercept and slope 3.3 Generalized Estimating equations
Learning activities and methodology
Learning activities: Master classes Exercises Computer labs Projects Teaching methodologies: Presentations of the professor in class with computing and visual media, where the professor develops the mail concepts of the subject and provides bibliography supplementing the knowledge of students. Critical reading
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Balakrishnan, N.. Methods and Applications of Statistics in Clinical Trials. John Wiley & Sons. 2014
  • Hosmer, David W; Lemeshow, Stanley; May, Susanne. Applied Survival Analysis: Regression Modeling of Time to Event Data. Wiley-Interscience. 2008
  • Moore, D.F.. Applied Survival Analysis Using R. Springer. 2016
  • Singer, Judith D; Willet, John B. Applied longitudinal data analysis : modeling change and event occurrence. Oxford University Press. 2003

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