Erasmus Summer Programme Courses
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Introduction to Data-analysis [ESP03]
6 August 2018
10 August 2018
Monday to Friday (5 afternoons)
From 13:00 till 18:00
Prof. Adelin Albert
Erasmus MC, Rotterdam NL
An elementary knowledge in statistics acquired during a bachelor or master university degree.
- Clinical Research
- Clinical Epidemiology
Course materials will be available online, download instructions, in particular regarding the installation of R and the R Commander, will be sent in August by e-mail and made available on Canvas.
Participants are required to bring their own laptop with the battery fully charged.
Detailed information about this course:
Faculty: Prof. Adelin Albert, PhD
This course is a general introduction to the basics of statistics used in biomedical and public health applications. We start with a general definition of statistics and give some examples. We then review the notions of population, sample, variables (qualitative and quantitative) and data (missing, outlying, and censored). Next, the course focusses on ways to describe data such as tables, graphs, distributions and summary statistics (mean, standard deviation, median, quartiles) as reported in medical journals. Lifetime data will be visualized graphically by the celebrated Kaplan-Meier survival curve. Association measures between variables (correlation, regression, relative risk, odds ratio and hazard ratio) as well as agreement measures between observers (Cohen kappa coefficient) will be discussed.
The course will then turn on the relation between the population and the random sample and on how characteristics observed in the sample can be generalized to the population. Some elementary probability elements will be needed here. This will lead to the important concepts of standard error and confidence intervals (for means, proportions, odds ratios, hazard ratios). The general theory of hypothesis testing will be briefly outlined from an intuitive perspective and the fundamental concepts of statistical significance, power calculation and p-value will be introduced. Then, we shall review some of the most frequently used testing procedures: correlation test, unpaired and paired t-tests for comparing two means values, analysis of variance for comparing several means (with multiple tests correction), chi-squared test (Fisher exact test) for comparing two proportions and more generally for contingency tables, McNemar test for paired proportions, and two-way analysis of variance for repeated data. The logistic model and Cox model will be briefly alluded to because of their importance in the medical literature. Finally, the basic principles underlying non parametric tests will be outlined and some of the most used distribution-free tests presented (Spearman correlation, Wilcoxon signed rank test, Mann-Whitney U-test, Kruskal-Wallis and Friedman tests).
During the course, a brief introduction to the R statistical software will be given to participants. R is free of charge, increasingly used worldwide, but not easy to learn for the layman due to its tedious programming language. There is however a 'Point-and-Click' interface for R called the 'R Commander' or simply 'Rcmdr' which is really easy to learn and use. Thus, students will acquire some familiarity with the R Commander, do basic statistical calculations and draw nice graphs even on large datasets.
Topics covered in the course will be illustrated using real data from the medical literature. Participants will also use Rcmdr during the course.
Written exam on the Friday in the week after ESP (only for NIHES MSc students and for ‘keuzevak students’). Course materials are allowed during the examination. If other students wish to do this exam, they have to pay a fee of €75,- per exam. Credits are 1.0 ECTS when you take the exam, instead of 0.7 ECTS. Please contact NIHES (email@example.com) if you wish to register for the exam, at least two weeks before the start of the course.
This course is equivalent to Biostatistics for Clinicians (EWP22) and Biostatistical Methods I: basic principles, part A (CC02A).
- To have a clear understanding of what statistics is all about in medicine and public health, and to be acquainted with the most commonly statistical methods in the biomedical literature.
- To be able to assess when and how to apply these methods in real-life situations.
- To improve skills in data presentation, interpretation and communication.
- To perceive the importance of data analysis with respect to experimental planning, data collection, data reporting and data interpretation.
- To perform basic statistical analyses and graphs using the statistical \"Point-and-Click\" software Rcmdr even if familiar with well-known statistical packages as SAS, SPSS or Stata.
- Students and researchers who want to have a quick refreshment of basic statistical concepts and methods
- Physicians and healthcare professionals who need some intelligible introduction to statistics
- Any person who wants a broad overview of statistical issues and methods in health sciences
Attendance, Written exam