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41129-01 - Vorlesung mit Übungen: Advanced Research Methods - Using large, routine data for health services research 3 KP

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 14-täglich
Datum 10.03.2021 – 26.05.2021
Zeit 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

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