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Citation link: http://nbn-resolving.de/urn:nbn:de:gbv:46-00105995-18
00105995-1.pdf
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Designs and analytical strategies to control for unmeasured confounding in studies based on administrative health care databases


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Other Titles: Designs und analytische Strategien zur Kontrolle ungemessenen Confoundings in Studien basierend auf administrativen Gesundheitsdatenbanken
Authors: Enders, Dirk  
Supervisor: Pigeot-Kübler, Iris
1. Expert: Pigeot-Kübler, Iris
2. Expert: Stürmer, Til 
Abstract: 
Studies based on routine data of statutory health insurances require the adequate consideration of unmeasured confounders. This thesis investigated two methods to cope with this problem: (i) Classic two-phase designs collect additional data for a stratified subset (phase 2) of all patients (phase 1). An extension was proposed that does not need a stratification of the data but a proper model for participation in phase 2. A simulation study comparing the extended method with multiple imputation (MI) as an alternative revealed that MI resulted in less biased and more precise estimators of the treatment effect. (ii) The high-dimensional propensity score (HDPS) algorithm automatically selects hundreds of empirical confounders from the underlying database, but was mainly applied in pharmacoepidemiology. This thesis investigated the HDPS algorithm in a study in health services research, where a shift in the effect estimates towards a more plausible result was achieved. The further development of these or similar methods is needed in the near future, since studies based on linking routine data with other data source are gaining in importance.
Keywords: High-dimensional propensity score, Routine health care, Two-phase, Unmeasured confounding
Issue Date: 21-Jul-2017
Type: Dissertation
URN: urn:nbn:de:gbv:46-00105995-18
Institution: Universität Bremen 
Faculty: FB3 Mathematik/Informatik 
Appears in Collections:Dissertationen

  

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