ArticleStudies in health technology and informatics2024
Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.
Article in Studies in health technology and informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Advancing multi-database cohort studies: lessons learned from a studyathon in a pharmaceutical company.JAMIA open · 2026Article
- Evaluating large-scale propensity score adjustment when sample size is small.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Association of Herpes Simplex Virus Type-1 With Dementia Outcomes: Longitudinal Retrospective Cohort Study Using Real-World Electronic Health Record Data.JMIR formative research · 2026Article
- Heterogeneity of Treatment Effects Across Nine Glucose-Lowering Drug Classes in Type 2 Diabetes: Extension of the LEGEND-T2DM Network Study.Diabetes, obesity & metabolism · 2026Observational
- Active Safety Surveillance Using Real-World Evidence (ASSURE) Implementation: Transparent and Reproducible Real-World Evidence Standardized Framework to Support Safety Signal Evaluation.Pharmacoepidemiology and drug safety · 2026Article
- From study design to executable code: automating target trial emulation with large language models.JAMIA open · 2026Article
- Real-world performance of large-scale propensity score adjustment strategies: Matching, weighting, and stratification.Research square · 2026Article
- Measuring the Quality of Datasets: Development of the IDEFIM Indicator Set for Empirical Health Research.Journal of medical Internet research · 2026Article
- Comparative Cardiovascular Effectiveness of Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors in Diabetes Mellitus.Journal of the American College of Cardiology · 2026Article
- Implementation of OMOP and ConcePTION Common Data Models in CPRD GOLD: Risk of Bleeding and Cardiovascular Outcomes From Anticoagulant Use.Clinical pharmacology and therapeutics · 2026Article
- Comparison of Cardiovascular Events in Patients Receiving Concomitant Clopidogrel and Proton Pump Inhibitors Classified by CYP2C19 Inhibitory Potency.Arteriosclerosis, thrombosis, and vascular biology · 2026Observational
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Observational
- Real-world evidence for comparative safety of second-line antihyperglycemic agents in older adults with type 2 diabetes.Nature communications · 2026Article
- CohortCharacteristics: an R package for population characterisation in observational studies using the OMOP common data model.European journal of epidemiology · 2026Article
- Health care utilization and costs associated with acute exacerbation of fibrosing interstitial lung disease in the United States: A retrospective administrative claims database analysis.Journal of managed care & specialty pharmacy · 2026Article
- Heterogeneity of Treatment Effects Across Nine Glucose-Lowering Drug Classes in Type 2 Diabetes: Extension of the LEGEND-T2DM Network Study.medRxiv : the preprint server for health sciences · 2026Article
- Clinical implementation of an AI-based prediction model for decision support for patients undergoing colorectal cancer surgery.Nature medicine · 2025Article
- Real-world Evidence on Baseline Characteristics and Treatment in Metastatic Hormone-sensitive Prostate Cancer: Findings from the PIONEER 2.0 Big Data Investigation Group.European urology open science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.
Indexed as
Identifiers
What OpenQuestion holds
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.