Observational studyStatistical methods in medical research2020
Comparing the high-dimensional propensity score for use with administrative data with propensity scores derived from high-quality clinical data.
Observational study in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 25 citations in OpenAlex.
- Impact of empiric potassium supplementation on mortality, sudden cardiac arrest and stroke in furosemide initiators.British journal of clinical pharmacology · 2026Article
- Immigration Status and Time to Accessing Publicly Funded Flash Glucose Monitoring Systems.JAMA network open · 2026Article
- High-Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome.Pharmacoepidemiology and drug safety · 2025Article
- Improving ACS prediction in T2DM patients by addressing false records in electronic medical records using propensity score.Scientific reports · 2025Article
- The use of gabapentinoids and opioids and risk of developing opioid-induced respiratory depression among older breast cancer survivors with neuropathic pain.Journal of cancer survivorship : research and practice · 2024Article
- Economic and Clinical Burden of Herpes Zoster Among Patients With Inflammatory Bowel Disease in the United States.Crohn's & colitis 360 · 2023Article
- 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
- How does cirrhosis impact mortality, morbidity, and resource utilization in non-variceal upper gastrointestinal bleeding? A nationwide analysis.Przeglad gastroenterologiczny · 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
- Is acid suppression associated with the increased length of stay in diabetic ketoacidosis patients? A nationwide analysis.Journal of internal medicine · 2022Article
- A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.Pharmacoepidemiology and drug safety · 2022Article
- Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting.Pharmacoepidemiology and drug safety · 2022Article
- Change in healthcare during Covid-19 pandemic was assessed through observational designs.Journal of clinical epidemiology · 2022Observational
- Statin Discontinuation and Cardiovascular Events Among Older People in Denmark.JAMA network open · 2021Article
- Article
- Analysis of patients with Crohn's disease and intestinal obstruction: a National Inpatient Sample study.Annals of gastroenterologyArticle
- Comparison of outcomes between variceal and non-variceal gastrointestinal bleeding in patients with cirrhosis: Insights from a Nationwide Inpatient Sample.Annals of gastroenterologyArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Administrative healthcare databases are increasingly being used for research purposes. When used to estimate the effects of treatments and interventions, an important limitation of these databases is the lack of information on important confounding variables. The high-dimensional propensity score (hdPS) is an algorithm that generates a large number of empirically-derived covariates using administrative healthcare databases. The hdPS has been described as enabling adjustment by proxy, in which a large number of empirically-derived covariates may serve as proxies for unmeasured confounding variables. We examined the validity of this assumption using samples of patients hospitalized with acute myocardial infarction (AMI) and congestive heart failure (CHF), for whom both administrative data and detailed clinical data were available. We considered three treatments in AMI patients: angiotensin-converting enzyme inhibitors, beta-blockers, and statins, while the first two treatments were also considered in CHF patients. We considered three propensity scores: (a) one derived using detailed clinical data; (b) the hdPS derived from administrative data; and (c) one derived from administrative data using expert opinion. Using each propensity score, we estimated inverse probability of treatment (IPT) weights. For each sample and treatment combination, and for each of the two propensity scores derived using administrative data, there were clinical variables not measured in administrative data that remained imbalanced after incorporating the IPT weights. However, the propensity score derived using clinical data always resulted in all clinical variables being balanced. When estimating hazard ratios, for some samples and treatment combinations, the hazard ratios estimated using the hdPS were more similar to those obtained using the clinical propensity score than were those obtained using the expert-derived propensity score. However, for other combinations, the effects estimated using the expert-derived propensity score were more similar to those obtained using the clinical propensity score than were those derived using the hdPS.
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