ReviewClinical pharmacology and therapeutics2020
Analytic and Data Sharing Options in Real-World Multidatabase Studies of Comparative Effectiveness and Safety of Medical Products.
Review in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses 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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Distributed Cox proportional hazards regression using summary-level information.Biostatistics (Oxford, England) · 2023Pooled it
- A systematic review of how missing data are handled and reported in multi-database pharmacoepidemiologic studies.Pharmacoepidemiology and drug safety · 2021Pooled it
- 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
- Distributed fusion R-learner of heterogeneous treatment effect using distributed medicaid data.Biometrics · 2026Article
- Review
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- Real-World Research on Retinal Diseases Using Health Claims Database: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Toward a common standard for data and specimen provenance in life sciences.Learning health systems · 2024Article
- Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.Journal of the American Statistical Association · 2023Article
- Invited Commentary: Go BIG and Go Global-Executing Large-Scale, Multisite Pharmacoepidemiologic Studies Using Real-World Data.American journal of epidemiology · 2022Article
- Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes.Lifetime data analysis · 2022Article
- Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Privacy-preserving data sharing infrastructures for medical research: systematization and comparison.BMC medical informatics and decision making · 2021Article
- Data Integration in Causal Inference.Wiley interdisciplinary reviews. Computational statisticsArticle
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
Abstract
A wide range of analytic and data sharing options are available in nonexperimental multidatabase studies designed to assess the real-world benefits and risks of medical products. Researchers often consider six scientific domains when choosing among these options-study design, exposure type, outcome type, covariate summarization technique, covariate adjustment method, and data sharing approach. This article reviews available analytic and data sharing options and discusses key scientific and practical considerations when choosing among these options in multidatabase studies of comparative effectiveness and safety of medical products. The scientific considerations must be balanced against what the data-contributing sites are able or willing to share. While pooling of person-level data sets remains the most familiar and analytically flexible approach, newer analytic and data sharing approaches that share less granular summary-level information may be equally valid and preferred in some multidatabase studies, especially when sharing of person-level data is challenging or infeasible.
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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.