ArticleEuropean journal of clinical pharmacology2016
Performance of the high-dimensional propensity score in adjusting for unmeasured confounders.
Article in European journal of clinical pharmacology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- High-Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome.Pharmacoepidemiology and drug safety · 2025Article
- Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies.BMC medical research methodology · 2025Article
- Targeted learning with an undersmoothed LASSO propensity score model for large-scale covariate adjustment in health-care database studies.American journal of epidemiology · 2024Article
- Article
- Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia.Pharmacoepidemiology and drug safety · 2024Article
- Evaluating the Performance of High-Dimensional Propensity Scores Compared with Standard Propensity Scores for Comparing Antihypertensive Therapies in the CPRD GOLD Database.Cardiology and therapy · 2023Article
- High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.Pharmacoepidemiology and drug safety · 2023Article
- Restricted Mean Survival Time Analysis to Estimate SGLT2i-Associated Heterogeneous Treatment Effects on Primary and Secondary Prevention of Cardiorenal Outcomes in Patients With Type 2 Diabetes in Taiwan.JAMA network open · 2022Article
- Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.Pharmacoepidemiology and drug safety · 2022Review
- DOUBLY DEBIASED LASSO: HIGH-DIMENSIONAL INFERENCE UNDER HIDDEN CONFOUNDING.Annals of statistics · 2022Article
- A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.Pharmacoepidemiology and drug safety · 2022Article
- Comparative Safety of Dipeptidyl Peptidase-4 Inhibitors and Sudden Cardiac Arrest and Ventricular Arrhythmia: Population-Based Cohort Studies.Clinical pharmacology and therapeutics · 2022Article
- Comparative cardiometabolic risk of antipsychotics in children, adolescents and young adults.European child & adolescent psychiatry · 2021Article
- Prenatal antibiotic exposure and risk of attention-deficit/hyperactivity disorder: a population-based cohort study.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2020Article
- The association of beta-blocker use with mortality in elderly patients with congestive heart failure and advanced chronic kidney disease.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2020Article
- The risk of sudden cardiac arrest and ventricular arrhythmia with rosiglitazone versus pioglitazone: real-world evidence on thiazolidinedione safety.Cardiovascular diabetology · 2020Observational
- Comparing the high-dimensional propensity score for use with administrative data with propensity scores derived from high-quality clinical data.Statistical methods in medical research · 2020Observational
- A theoretical exploration of therapeutic monomania as a physician-based instrumental variable.Pharmacoepidemiology and drug safety · 2020Review
- Association between perioperative oral care and postoperative pneumonia after cancer resection: conventional versus high-dimensional propensity score matching analysis.Clinical oral investigations · 2019Article
- Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data.Statistical methods in medical research · 2019Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeHigh-dimensional propensity scores (hdPS) can adjust for measured confounders, but it remains unclear how well it can adjust for unmeasured confounders. Our goal was to identify if the hdPS method could adjust for confounders which were hidden to the hdPS algorithm.
methodThe hdPS algorithm was used to estimate two hdPS; the first version (hdPS-1) was estimated using data provided by 6 data dimensions and the second version (hdPS-2) was estimated using data provided from only two of the 6 data dimensions. Two matched sub-cohorts were created by matching one patient initiated on a high-dose statin to one patient initiated on a low-dose statin based on either hdPS-1 (Matched hdPS Full Info Sub-Cohort) or hdPS-2 (Matched hdPS Hidden Info Sub-Cohort). Performances of both hdPS were compared by means of the absolute standardized differences (ASDD) regarding 18 characteristics (data on seven of the 18 characteristics were hidden to the hdPS algorithm when estimating the hdPS-2).
resultsEight out of the 18 characteristics were shown to be unbalanced within the unmatched cohort. Matching on either hdPS achieved adequate balance (i.e., ASDD <0.1) on all 18 characteristics.
conclusionOur results indicate that the hdPS method was able to adjust for hidden confounders supporting the claim that the hdPS method can adjust for at least some unmeasured confounders.
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