DQAgui

DQAgui provides data quality assessment for observational research by organizing and analyzing tabular secondary-use datasets to evaluate conformance, completeness, and plausibility.

Key Features:

• Implementation: Implemented in the R programming language.
• Data quality framework: Organizes results according to Kahn et al.'s data quality categories—conformance, completeness, and plausibility.
• Analytical capabilities: Performs systematic evaluation of tabular datasets with options to define time periods and restrict analyses to specific data elements.
• Datamap generation: Produces a centralized datamap of data element availability spanning two years for network-level comparison.

Scientific Applications:

• Observational data quality assessment: Facilitates assessment of secondary-use clinical and biomedical data to support reliable and valid analyses.
• Multi-site monitoring: Supports comparison of data element availability and quality across participating sites within research networks.

Methodology:

Organizes DQ results according to Kahn et al.'s categories (conformance, completeness, plausibility); implements systematic evaluation of tabular datasets with options to define time periods and select specific data elements; implemented in R; generates a two-year datamap of data element availability.