Introductory Biostatistics

   IMPORTANT DATES for this Course
   Deadline for applications: Oct 30th 2018
   Course dates: Nov 26th - Nov 30th 2018

Candidates with adequate profile will be accepted in the next 72 hours after the application, until we reach 20 participants.


Ana Cristina Paulo is a post-doctoral researcher at Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA). She holds a PhD in Population Biology from Faculdade de Ciências, Universidade de Lisboa (FCUL), a Master's degree in Probability and Statistics (FCUL) and a 5-year degree in Biology (FCUL). She trained undergraduate students in biostatistics, exploratory data analysis and statistics and was facilitator for the statistical courses of time-series analysis and multivariate statistics organized by the European Intervention Epidemiology Training (EPIET) from the European Centre for Disease Prevention and Control (ECDC). During her post-doctoral studies she developed computational tools to study the spread of transmissible childhood diseases and has applied statistical methods to the analysis and interpretation of epidemiological studies.

Affiliation: Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, PT

Marta Belchior Lopes is a post-doctoral researcher at Instituto de Engenharia Mecânica from Instituto Superior Técnico, Universidade de Lisboa (IST-UL). She holds a PhD in Biotechnology from IST-UL, a Master's degree in Applied Mathematics in Biological Sciences from Instituto Superior de Agronomia (UL) and a 5-year degree in Biology from Faculdade de Ciências (UL). During her post- doctoral studies, she has been working on the development of mathematical and computational tools in biomedical and biopharmaceutical applications, based on the extraction of meaningful information by data mining and machine learning from data generated by high-throughput technologies. Her current research activities are inserted in the H2020 Statistical Multi-Omics Understanding (SOUND) project goal of creating bioinformatic tools for statistically informed use of personal genomic and other ’omic data in medicine, in collaboration with leading institutions in personalized medicine including EMBL Heidelberg, ETH and University Hospital Zurich, TU Munich and TUM-MED, University of Cambridge, and the Roswell Park Cancer Comprehensive Center.

Affiliation: Instituto de Engenharia Mecânica, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, PT

Course description

This is one of our "Foundations" type courses, providing a systematic and detailed review of fundamental concepts and techniques in Biostatistics. Participants can expect to go through a set of exercises that are based on biomedical problems. These exercises are preceded by short lectures that are simple to follow. The lectures are designed to provide the conceptual framework that is needed to release the training power of the exercises, not to flood the participants with formality, which will be kept to a minimum. We will make use of a highly interactive methodology, taking advantage of our well equipped Bioinformatics training room. With this approach, we expect to bring the participants to a high degree of usage independence in using the methods that we cover.
Main topics: Descriptive Statistics; Probability; Statistical Inference; Design of Experiments.
See the Detailed Program .

Target Audience

Most participants must have had one or more semester courses in Statistics in their graduate education. For some, learning Statistics has happened a long time ago, and that makes it difficult to go back and manipulate the concepts with full confidence. For others, school days were just a while ago, but using that knowledge represents a serious effort.
Opening a Statistics handbook, when you want to use Statistics righ away, is usually a painful experience, as concepts often show a less than obvious interdependence. Attending this course is a chance of revisiting those subjects in a systematic way, through a series of hands-on exercises, in a workflow that brings-in the skills in a seamless way.

Course Pre-requisites

Basic knowledge in Statistics is handy.
Elementary skills in computer usage are needed.
Basic Familiarity with the R environment will be necessary. Please follow the exercise that we provide. Install R from http://cran.r-project.org/ following the instructions. Download and unzip the Tutorial folder that is made available here.
- Visualize the slides in "Tutorial R.pdf"
- Follow the exercise in "Basic_Exercise.pdf"
- For reference, we also provide a script with a correct set of R statements in sequence "Tutorial_script.R"

Note: the dataset required in step17 is no longer available for download from the specified source; therefore, it is provided in the zipped folder of this tutorial.


Detailed Program

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated:   Aug 7th 2018