Volume 53 | Number 1 | February 2018

Abstract List

Dirk Enders Dipl.‐Math., Christoph Ohlmeier Dr. PH, Edeltraut Garbe Dr. Med.


Evaluating the potential of the high‐dimensional propensity score () to control for residual confounding in studies analyzing quality of care based on administrative health insurance data.

Data Source

Secondary data from 2004 to 2009 from three German statutory health insurance providers.

Study Design

We conducted a retrospective cohort study in patients with elective percutaneous coronary interventions (s) and compared the mortality risk between the in‐ and outpatient setting using Cox regression. Adjustment for predefined confounders was performed using conventional propensity score () techniques. Further, an was calculated based on predefined and empirically selected confounders from the database.

Principal Findings

Conventional methods showed a decreased mortality risk for outpatient compared to inpatient s, while trimming of patients with nonoverlap in the distribution and weighting resulted in a comparable risk. Most comorbidities were less prevalent in the ‐trimmed population compared to the original one.


The methodology may reduce residual confounding by rendering the studied cohort more comparable through restriction. However, results cannot be generalized for the entire study population. To provide unbiased results, full assessment of all unmeasured confounders from proxy information in the database would be necessary.