ArticleHarvard data science review2020
How Confident Are We about Observational Findings in Healthcare: A Benchmark Study.
Article in Harvard data science review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Pooled it
- Evaluating large-scale propensity score adjustment when sample size is small.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Negative controls and how to use them: a guidance paper.International journal of epidemiology · 2026Article
- 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
- When evidence meets artificial intelligence.Lancet regional health. Americas · 2026Review
- Real-world performance of large-scale propensity score adjustment strategies: Matching, weighting, and stratification.Research square · 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
- Semaglutide and diabetic retinopathy: an OHDSI network study.BMJ open diabetes research & care · 2025 · on this mapObservational
- Performance of the Self-Controlled Case Series With Active Comparators for Drug Safety Signal Detection Using the French Administrative Healthcare Database (SNDS).Pharmacoepidemiology and drug safety · 2025Article
- Real-World Effectiveness of Omalizumab Treatment in Adult Asthma Patients.Yonsei medical journal · 2025Article
- The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to RemicadeBMC medical research methodology · 2024Article
- Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.medRxiv : the preprint server for health sciences · 2024Article
- Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM.Journal of the American College of Cardiology · 2024Article
- Article
- Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.Annals of internal medicine · 2024Observational
- Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.Studies in health technology and informatics · 2024Article
- Automating assignment of HIV+ patients into phenogroups from demography bound phenotype attack rates.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Real-world Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.medRxiv : the preprint server for health sciences · 2023Article
- Using public clinical trial reports to probe non-experimental causal inference methods.BMC medical research methodology · 2023Article
- Adjusting for indirectly measured confounding using large-scale propensity score.Journal of biomedical informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
Abstract
Healthcare professionals increasingly rely on observational healthcare data, such as administrative claims and electronic health records, to estimate the causal effects of interventions. However, limited prior studies raise concerns about the real-world performance of the statistical and epidemiological methods that are used. We present the "OHDSI Methods Benchmark" that aims to evaluate the performance of effect estimation methods on real data. The benchmark comprises a gold standard, a set of metrics, and a set of open source software tools. The gold standard is a collection of real negative controls (drug-outcome pairs where no causal effect appears to exist) and synthetic positive controls (drug-outcome pairs that augment negative controls with simulated causal effects). We apply the benchmark using four large healthcare databases to evaluate methods commonly used in practice: the new-user cohort, self-controlled cohort, case-control, case-crossover, and self-controlled case series designs. The results confirm the concerns about these methods, showing that for most methods the operating characteristics deviate considerably from nominal levels. For example, in most contexts, only half of the 95% confidence intervals we calculated contain the corresponding true effect size. We previously developed an "empirical calibration" procedure to restore these characteristics and we also evaluate this procedure. While no one method dominates, self-controlled methods such as the empirically calibrated self-controlled case series perform well across a wide range of scenarios.
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.