dataquieR is an R package to conduct data quality assessments in data collections designed for research. It makes strong use of metadata that specify the requirements of the study data. Spreadsheet tables can be used to collect this information in a standardized manner. dataquieR starts with checking the formal compliance of study data with expectations defined in the metadata, such as the data type, during integrity analyses. Depending on available metadata, further data quality assessments cover the dimensions completeness, consistency, and accuracy as proposed by the framework of Schmidt et al. (2020). Three dataquieR functions investigate the completeness of data within and across observational units. Consistency-related analysis comprises two aspects. First, depending on the data type, the compliance of data elements with either user-defined limits or the adherence to expected value lists is investigated. Second, contradictions between data values of two data elements can be identified by using one of eleven logical comparisons, e.g., if systolic blood pressure is lower than diastolic blood pressure whereas the opposite is expected. Eight dataquieR functions support accuracy-related analyses by aiming at unexpected distributions of single or multiple data elements. Particular focus is placed on the influence of observers, examiners, and devices on the measurement process.
Source: Richter et al., (2021). dataquieR: assessment of data quality in epidemiological research. Journal of Open Source Software, 6(61), 3093, https://doi.org/10.21105/joss.03093
Das Produkt im Einsatz
- QS im Bereich MRT der NAKO-Gesundheitsstudie
- Testanwendung in MII
- Richter A, Schmidt CO, Krüger M, Struckmann S (2021): “dataquieR: assessment of data quality in epidemiological research,” Journal of Open Source Software, 6(61), 3093, doi:10.21105/joss.03093
- Schmidt CO, Struckmann S, Enzenbach C et al. (2021): “Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R,” BMC Medical Research Methodology 21, 63 (2021). doi:10.1186/s12874-021-01252-7
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