Skip navigation
SuUB logo
DSpace logo

  • Home
  • Institutions
    • University of Bremen
    • City University of Applied Sciences
    • Bremerhaven University of Applied Sciences
  • Sign on to:
    • My Media
    • Receive email
      updates
    • Edit Account details

Citation link: http://nbn-resolving.de/urn:nbn:de:gbv:46-00105848-17
00105848-1.pdf
OpenAccess
 
copyright

Controlling for unobserved confounders in observational studies using large health care databases by means of instrumental variables in time-to-event analysis


File Description SizeFormat
00105848-1.pdf1.49 MBAdobe PDFView/Open
Other Titles: Kontrolle für ungemessene Konfounder in Beobachtungsstudien basierend auf großen Gesundheitsdatenbanken mittels instrumenteller Variablen in der Überlebenszeitanalyse
Authors: Kollhorst, Bianca  
Supervisor: Pigeot-Kübler, Iris
1. Expert: Pigeot-Kübler, Iris
2. Expert: Abrahamowicz, Michal, PhD 
Abstract: 
Randomized controlled trials cannot provide all necessary information about drug reactions as they are limited by several factors. Observational studies in free-living populations are therefore necessary. Large health care databases are frequently used for this purpose. A common problem of analyses based on these databases is that confounding variables are often not recorded, so that effects are inconsistently estimated. Under certain conditions instrumental variables can eliminate confounding bias, so that IV estimators can consistently estimate treatment effects. Instrumental variable methods are well established for continuous outcomes using linear regression models, where two-stage least squares are typically used. However, in time-to-event analysis no such common instrumental variable method exists. Even if the proportional hazards model is used, two-stage estimators to account for instrumental variables are only justified for rare events. The aim of this thesis is therefore to explore two-stage instrumental variable estimation for time-to-event outcomes in large health care databases if the assumption of rare events does not hold true.
Keywords: Instrumental variables, unmeasured confounding, time-to-event analysis, health care databases
Issue Date: 21-Mar-2017
Type: Dissertation
URN: urn:nbn:de:gbv:46-00105848-17
Institution: Universität Bremen 
Faculty: FB3 Mathematik/Informatik 
Appears in Collections:Dissertationen

  

Page view(s)

101
checked on Feb 25, 2021

Download(s)

30
checked on Feb 25, 2021

Google ScholarTM

Check


Items in Media are protected by copyright, with all rights reserved, unless otherwise indicated.

Legal notice -Feedback -Data privacy
Media - Extension maintained and optimized by Logo 4SCIENCE