We review the notions of population, sample, variables (qualitative and quantitative) and data (missing, outlying, and censored). Next, the Introduction to Data-analysis course offered by Erasmus University Rotterdam will focus on modern ways to describe data such as tables, graphs, distributions and summary statistics (mean, standard deviation, median, quartiles), as required in the international scientific literature.
The analysis of survival data will also be envisaged, in particular the renowned Kaplan-Meier survival curve. Finally, the association between variables will be discussed (correlation, relative risk, odds ratio and regression) as well as the agreement between observers (Cohen kappa coefficient). The course will then turn on the relation between the population and the random sample and on how effects observed in the sample can be generalized to the total 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).
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 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 (and 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 international medical literature. The basic principles underlying non parametric tests will be outlined and the most used distribution-free tests mentioned (Spearman correlation, Wilcoxon signed rank test, Mann-Whitney U-test, Kruskal-Wallis and Friedman tests). All topics covered in the course will be illustrated using real data from the medical and biomedical literature and applied during practical sessions.
You can apply until:
Always verify the dates on the programme website.
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.
Prof. Adelin Albert, PhD
This programme requires students to demonstrate proficiency in English.Schedule IELTS
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
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