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41129-01 - Vorlesung mit Übungen: Advanced Research Methods - Using large, routine data for health services research (3 KP)
| Semester | Frühjahrsemester 2018 |
| Angebotsmuster | Jedes Frühjahrsem. |
| Dozierende | 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 provider profiling: Normand, S.-L. T., Glickman, M. E., & Gatsonis, C. A. (1997). Statistical Methods for Profiling Providers of Medical Care: Issues and Applications. Journal of the American Statistical Association, 92(439), 803-814. Berwick, D. M., James, B., & Coye, M. J. (2003). Connections between quality measurement and improvement. Med Care, 41(1 Suppl), I30-38. Krumholz, H. M., Wang, Y., Mattera, J. A., Wang, Y., Han, L. F., Ingber, M. J., . . . Normand, S.-L. T. (2006). An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With an Acute Myocardial Infarction. Circulation, 113(13), 1683-1692. doi:10.1161/circulationaha.105.611186 van Dishoeck, A.-M., Lingsma, H. F., Mackenbach, J. P., & Steyerberg, E. W. (2011). Random variation and rankability of hospitals using outcome indicators. BMJ Qual Saf, 20(10), 869-874. Books: 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, Daten und Code sind verfügbar per ADAM. Unterrichtssprache: Deutsch & Englisch |
| Weblink | Login ADAM |
| Teilnahmevoraussetzungen | Erfolgreicher Abschluss 2016 oder später der Lehrveranstaltungen Statistical Methods I (LV10537) & II (LV 10538) |
| Anmeldung zur Lehrveranstaltung | in MOnA belegen |
| Unterrichtssprache | Deutsch |
| Einsatz digitaler Medien | Online-Angebot obligatorisch |
| Intervall | Wochentag | Zeit | Raum |
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Keine Einzeltermine verfügbar, bitte informieren Sie sich direkt bei den Dozierenden.
| Module |
Modul Vertiefung Research (Master Pflegewissenschaft) |
| Prüfung | Lehrveranst.-begleitend |
| Hinweise zur Prüfung | 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 Prüfung | Anmelden: Belegen; Abmelden: Institut |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,1 |
| Belegen bei Nichtbestehen | einmal wiederholbar |
| Zuständige Fakultät | Medizinische Fakultät |
| Anbietende Organisationseinheit | Institut für Pflegewissenschaft |