ArticleAmerican journal of epidemiology2026
PEPRVision: a visualization framework for Pharmacoepidemiology research.
Article in American journal of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Medical chart review is commonly used in epidemiologic research to evaluate health outcomes, refine measurement algorithms, and identify potential study biases. In pharmacoepidemiologic research, administrative claims and other structured healthcare databases provide longitudinal detail suitable for similar purposes but are typically presented in tabular formats that are cumbersome to interpret. This study introduces PEPRVision, a visualization framework that transforms structured longitudinal healthcare data into intuitive graphics to support patient profile review in pharmacoepidemiology research. PEPRVision was developed based on principles of preattentive visual processing, leveraging visual attributes such as color, spatial position, shape, and length to facilitate rapid pattern recognition. We demonstrated PEPRVision using US administrative claims data through four case studies: (1) evaluating a claims-based algorithm for measurement of congenital hearing loss, (2) enhancing study design decisions by identifying reverse causation in a drug safety study, (3) assessing the face validity of signals of potential teratogenicity detected through data mining for triage for advancement to causal inference studies, and (4) identifying mechanisms of inappropriate prenatal exposure to teratogenic medications to inform improvement of risk mitigation strategies. As a complement to population-based causal inference studies, PEPRVision facilitates review of patient-level variation, enhancing the validity and clinical relevance of pharmacoepidemiologic research.
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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 Socratic graph.