ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015
Rediscovering drug side effects: the impact of analytical assumptions on the detection of associations in EHR data.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.Journal of biomedical informatics · 2021Article
- Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel.JCO clinical cancer informatics · 2020Article
- Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data.EGEMS (Washington, DC) · 2019Article
- Access to Routinely Collected Clinical Data for Research: A Process Implemented at an Academic Medical Center.Clinical and translational science · 2019Article
- Lost in Translation: Diagnosis Records Show More Inaccuracies After Biopsy in Oncology Care EHRs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Literature-Based Discovery of Confounding in Observational Clinical Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Exploring the Hazards of Scaling Up Clinical Data Analyses: A Drug Side Effect Discovery Case Report.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Large clinical datasets can be used to discover and monitor drug side effects. Many previous studies analyzed symptom data as discrete events. However, some drug side effects are inferred from continuous variables such as weight or blood pressure. These require additional assumptions for analysis. For example, we can define positive/negative thresholds and time windows within which we expect to see the side effect. In this paper, we discuss the impact of such assumptions on the ability to detect known continuous drug side effects using statistical and visualization techniques. Taking the case of prednisone exposure and weight gain reflected in real EHR data, we found that temporal windowing greatly affected the ability to detect the expected effect. Categorization of the exposure variable improved side effect detection but negatively impacted model fit. To avoid false positive and false negative conclusions from clinical data reuse, studies reusing clinical data should determine the sensitivity of their findings to alternative analytic assumptions.
Identifiers
26306235PMC4525264What 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.