ArticleJournal of comparative effectiveness research2016
Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records.
Article in Journal of comparative effectiveness research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
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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
17 citing papers in PubMed.
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- Screening for Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Type 2 Diabetes: Are We Doing Enough?Diabetes spectrum : a publication of the American Diabetes Association · 2025Article
- Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data.Molecular psychiatry · 2024Article
- Article
- Associations of Tinnitus Incidence with Use of Tumor Necrosis Factor-Alpha Inhibitors among Patients with Autoimmune Conditions.Journal of clinical medicine · 2023Article
- Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.Pharmacoepidemiology and drug safety · 2022Review
- Assessment of heterogeneous treatment effect estimation accuracy via matching.Statistics in medicine · 2021Article
- Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.Advances in nutrition (Bethesda, Md.) · 2021Article
- A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification.Network modeling and analysis in health informatics and bioinformatics · 2021Article
- A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.Statistics in medicine · 2020Article
- Chromosome arm aneuploidies shape tumour evolution and drug response.Nature communications · 2020Article
- Detecting Signals of Dietary Supplement Adverse Events from the CFSAN Adverse Event Reporting System (CAERS).AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Characterizing drug-related adverse events by joint analysis of biomedical and genomic data: A case study of drug-induced pulmonary fibrosis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- On Interestingness Measures for Mining Statistically Significant and Novel Clinical Associations from EMRs.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2016Article
- Data mining differential clinical outcomes associated with drug regimens using adverse event reporting data.Nature biotechnology · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
aimsElectronic health records (EHR), containing rich clinical histories of large patient populations, can provide evidence for clinical decisions when evidence from trials and literature is absent. To enable such observational studies from EHR in real time, particularly in emergencies, rapid confounder control methods that can handle numerous variables and adjust for biases are imperative. This study compares the performance of 18 automatic confounder control methods.
methodsMethods include propensity scores, direct adjustment by machine learning, similarity matching and resampling in two simulated and one real-world EHR datasets. RESULTS &
conclusionsDirect adjustment by lasso regression and ensemble models involving multiple resamples have performance comparable to expert-based propensity scores and thus, may help provide real-time EHR-based evidence for timely clinical decisions.
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Registered trials
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