Allgemeine Hinweise

Die maximale Teilnehmeranzahl für alle Turorials liegt bei 20 Personen.
Die Kosten belaufen sich auf 50,00 € für Mitglieder DGepi & Nichtmitglieder und für Studierende auf 40,00 €.

Adresse: Leibniz-Institut für Präventionsforschung und Epidemiologie (BIPS) |  Achterstraße 30 | 28359 Bremen

Dienstag, 25. September 2018

(entfällt) The ABC of scientific writing

10:00 – 17:00 Uhr The purpose of the tutorial is to equip participants with basic skills to help them present the results of their research in a way that will best convince the editors of journals, the reviewers and readers that their work is important and reliable.
The main idea is to help scientists structure their papers – that is, to arrange the necessary material into sections in a logical order so that the reader will be led through the arguments and thus understand the relevance of the results. Reference will be made to reporting guidelines such as STROBE and PRISMA. The fundamentals of manuscript structure are explained, discussed and illustrated using examples prepared from published literature and/or the participants’ own work if available.

Language: The tutorial will be offered in English.

Florence Samkange-Zeeb | samkange@bips.uni-bremen.de

An Introduction to Machine Learning in Epidemiology (Raum 1550 (1. Etage)

10:00 – 17:00 Uhr The course gives an intuitive introduction to the most important machine learning approaches used in epidemiology. The course is divided into theoretical and hands-on sessions. The focus of the theoretical sessions is a non-technical and intuitive explanation of machine learning algorithms and general concepts. In the hands-on sessions, the participants are instructed to perform basic machine learning analyses using R, compare the machines and perform hyperparameter tuning.

Prerequisites: Basic knowledge of R and a laptop with R installed for the hands-on sessions. No mathematics background required.

Language: English (or German if preferred by the audience)

Marvin N. Wright (Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen) and Damian Gola (Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck)

Introduction to Statistical Methods for Causal Inference (Raum 0490 (2. Etage)

10:00 – 17:00 Uhr Causal inference is about how and under what assumptions we can analyse observational data, such as cohort or registry data, with the aim to derive recommendations, either for individuals or policy makers. This requires specific assumptions, such as “no unobserved confounding” but also specialised methods, such as propensity scores or inverse probability weighting.
This course will give an overview of basic notions and approaches of causal inference. It will cover: 1) Causal DAGs 2) Confounding and methods of adjustment (regression, propensity scores, inverse probability weighting), incl. in time-dependent data 3) Selection bias 4) Instrumental variables (e.g. Mendelian randomization).
We will focus on principles and examples rather than technical details. There will be small (pen & paper) exercises for the participants (no laptops required).

Language: English (or German if preferred by the audience)

Vanessa Didelez and Janine Witte