ReviewClinical epidemiology2018
Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects.
Review in Clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed.
- Comparative Effectiveness and Safety of Generic Versus Brand-Name Fluticasone-Salmeterol to Treat Chronic Obstructive Pulmonary Disease.Annals of internal medicine · 2023Trial
- Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.BMC medical research methodology · 2026Article
- Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection.Statistics in medicine · 2026Article
- Fluticasone- vs Budesonide-Based Dual Therapy for COPD.JAMA network open · 2026Article
- High-dimensional multiple imputation for partially observed confounders including natural language processing-derived auxiliary covariates.American journal of epidemiology · 2026Article
- Applying High-Dimensional Propensity Scores in a Study of Inhaled Corticosteroids and COVID-19 Outcomes.Pharmacoepidemiology and drug safety · 2025Article
- Comparative effectiveness of robot-assisted vs. open prostatectomy: a real-life nationwide study.World journal of urology · 2025Observational
- The comparative effectiveness and safety of fluticasone-salmeterol via metered-dose versus dry powder inhalers for COPD: A new user cohort study.PLoS medicine · 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
- Mapping the effectiveness and risks of GLP-1 receptor agonists.Nature medicine · 2025 · on this mapArticle
- A future of data-rich pharmacoepidemiology studies: transitioning to large-scale linked electronic health record + claims data.American journal of epidemiology · 2025Article
- Article
- Discriminative Ability of the Charlson Comorbidity Index for Long-Term Mortality in a General Population: Nationwide, Population-Based Study of 10 Million Adults in Sweden.Clinical epidemiology · 2025Article
- Comparative effectiveness and safety of single inhaler triple therapies for chronic obstructive pulmonary disease: new user cohort study.BMJ (Clinical research ed.) · 2024Article
- Mismatch Repair Status and Surgical Outcomes in Localized Colorectal Cancer: A Nationwide Cohort Study.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Article
- Targeted learning with an undersmoothed LASSO propensity score model for large-scale covariate adjustment in health-care database studies.American journal of epidemiology · 2024Article
- Long-term risk of psychiatric disorder and psychotropic prescription after SARS-CoV-2 infection among UK general population.Nature human behaviour · 2024Article
- Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.BMC medical research methodology · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDecision makers in health care increasingly rely on nonrandomized database analyses to assess the effectiveness, safety, and value of medical products. Health care data scientists use data-adaptive approaches that automatically optimize confounding control to study causal treatment effects. This article summarizes relevant experiences and extensions.
methodsThe literature was reviewed on the uses of high-dimensional propensity score (HDPS) and related approaches for health care database analyses, including methodological articles on their performance and improvement. Articles were grouped into applications, comparative performance studies, and statistical simulation experiments.
resultsThe HDPS algorithm has been referenced frequently with a variety of clinical applications and data sources from around the world. The appeal of HDPS for database research rests in 1) its superior performance in situations of unobserved confounding through proxy adjustment, 2) its predictable efficiency in extracting confounding information from a given data source, 3) its ability to automate estimation of causal treatment effects to the extent achievable in a given data source, and 4) its independence of data source and coding system. Extensions of the HDPS approach have focused on improving variable selection when exposure is sparse, using free text information and time-varying confounding adjustment.
conclusionSemiautomated and optimized confounding adjustment in health care database analyses has proven successful across a wide range of settings. Machine-learning extensions further automate its use in estimating causal treatment effects across a range of data scenarios.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.