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| Semester | Frühjahrsemester 2021 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende |
Catherine Blatter (catherine.blatter@unibas.ch)
Sarah Naima Musy (sarah.musy@unibas.ch) Michael Simon (m.simon@unibas.ch, BeurteilerIn) |
| Inhalt | • A lecture-seminar-workshop format will be used, with 1-2 hrs of lectures, 2 hrs seminars and 1 hrs workshop. • The complete process from importing, preparing, analyzing, reporting and presenting the data will be covered. • Regression-based techniques will be introduced, applied and discussed • Examples will be introduced that showcase the unique design features to be considered in the planning and stages of a study so that specific methods can be employed during the analysis. |
| Lernziele | Using large, routine data (LRD) for health services research is part of the module on "Advanced Research Methods" which also introduces students to advanced research methods including the development and evaluation of complex healthcare interventions and health economics. The general objectives of the LRD course is to enhance the in-depth understanding of the planning and implementation of the analysis of large routine data in the context of health services research. Today’s healthcare systems provide a wide range of data sources like large discharge datasets, epidemiological registries or data from the electronic health record, which offer many opportunities for health and nursing research. The aim of this 3KP course is to provide an introduction in the analysis of large data sets for research purposes and to give students first hand experience in the analyses of such data sets. The course will provide students the basis to plan and conduct LRD analyses in the context of their own area of research. With the successful completion of the course students will be able to: 1. Understand the basic steps in the analytical process of LRD sets 2. Develop and assess answerable research questions in the context of LRD 3. Evaluate scope and limitations of popular analytical techniques in the context of LRD 4. Understand and apply principles of reproducible research 5. Plan, conduct and present a contained LRD project |
| Literatur | Please bring your own laptop with installed R and RStudio. 1) Install R: https://cran.r-project.org/ 2) Install RStudio: https://www.rstudio.com/products/rstudio/download/ If you haven’t used R before, please consider to participate in one of the numerous online tutorials to familiarize yourself with it. We will give a short R introduction in the beginning. To get everything set-up: 1) Datacamp: https://www.datacamp.com/courses/free-introduction-to-r 2) EdX: https://www.edx.org/course/explore-statistics-r-kix-kiexplorx-0 Helpful Sources • R reference card on ADAM • Cheat sheets on ADAM Papers on PS matching: • Peter C. Austin (2011) An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies, Multivariate Behavioral Research, 46:3, 399-424, DOI: 10.1080/00273171.2011.568786 • SEKHON, Jasjeet S. Multivariate and propensity score matching software with automated balance optimization: the matching package for R. 2011. Journal of Statistical Software, June 2011, Volume 42, Issue 7. Useful books (on regression models – not for PS matching!): • Fox, J., & Weisberg, S. (2010). An R companion to applied regression. Sage. • Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models: Cambridge University Press. |
| Bemerkungen | Folien und Daten sind verfügbar per ADAM. Unterrichtssprache: Deutsch & Englisch |
| Weblink | Login ADAM |
| Teilnahmebedingungen | Erfolgreicher Abschluss 2016 oder später der Lehrveranstaltungen Statistical Methods I (LV10537) & II (LV 10538) |
| Anmeldung zur Lehrveranstaltung | in MOnA belegen |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | Online-Angebot obligatorisch |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| 14-täglich | Mittwoch | 09.15-16.00 | - Online Präsenz - |
| Datum | Zeit | Raum |
|---|---|---|
| Mittwoch 10.03.2021 | 09.15-16.00 Uhr | - Online Präsenz -, -- |
| Mittwoch 24.03.2021 | 09.15-16.00 Uhr | - Online Präsenz -, -- |
| Mittwoch 14.04.2021 | 09.15-12.00 Uhr | - Online Präsenz -, -- |
| Mittwoch 28.04.2021 | 09.15-16.00 Uhr | - Online Präsenz -, -- |
| Mittwoch 05.05.2021 | 09.15-16.00 Uhr | - Online Präsenz -, -- |
| Mittwoch 26.05.2021 | 09.15-12.00 Uhr | - Online Präsenz -, -- |
| Module |
Modul Vertiefung Research (Masterstudium: Pflegewissenschaft) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | The exams consist of a written analysis plan (pass/fail, 10% of the course grade) and a written report of the analysis (3000 words, 90% of the course grade). |
| An-/Abmeldung zur Leistungsüberprüfung | Anmelden: Belegen; Abmelden: Institut |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,1 |
| Wiederholtes Belegen | einmal wiederholbar |
| Zuständige Fakultät | Medizinische Fakultät |
| Anbietende Organisationseinheit | Institut für Pflegewissenschaft |