ArticleHealth services research2018
The Potential of High-Dimensional Propensity Scores in Health Services Research: An Exemplary Study on the Quality of Care for Elective Percutaneous Coronary Interventions.
Article in Health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.Pharmacoepidemiology and drug safety · 2023Article
- Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.Statistical methods in medical research · 2019Article
- Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects.Clinical epidemiology · 2018Review
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Authors and funding
3 authors.
Funding
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Abstract
objectiveEvaluating the potential of the high-dimensional propensity score (HDPS) 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
designWe conducted a retrospective cohort study in patients with elective percutaneous coronary interventions (PCIs) and compared the mortality risk between the in- and outpatient setting using Cox regression. Adjustment for predefined confounders was performed using conventional propensity score (PS) techniques. Further, an HDPS was calculated based on predefined and empirically selected confounders from the database. PRINCIPAL
findingsConventional PS methods showed a decreased mortality risk for outpatient compared to inpatient PCIs, while trimming of patients with nonoverlap in the HDPS distribution and weighting resulted in a comparable risk. Most comorbidities were less prevalent in the HDPS-trimmed population compared to the original one.
conclusionThe HDPS 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.
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