ArticlePharmacology research & perspectives2017
Data visualizations to detect systematic errors in laboratory assay results.
Article in Pharmacology research & perspectives, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP).CPT: pharmacometrics & systems pharmacology · 2021Article
- Wrangling environmental exposure data: guidance for getting the best information from your laboratory measurements.Environmental health : a global access science source · 2019Article
- Data visualizations to detect systematic errors in laboratory assay results.Pharmacology research & perspectives · 2017Article
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1 author.
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Abstract
The measurement of concentrations of drugs and endogenous substances is widely used in basic and clinical pharmacology research and service tasks. Using data science-derived visualizations of laboratory data, it is demonstrated on a real-life example that basic statistical exploration of laboratory assay results or advised standard visual methods of data inspection may fall short in detecting systematic laboratory errors. For example, data pathologies such as generating always the same value in all probes of a particular assay run may pass undetected when using standard methods of data quality check. It is shown that the use of different data visualizations that emphasize different views of the data may enhance the detection of systematic laboratory errors. A dotplot of single data in the order of assay is proposed that provides an overview on the data range, outliers and a particular type of systematic errors where similar values are wrongly measured in all probes.
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Registered trials
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