Study-Specific Quality, Utility, and Breadth Assessment


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dc.contributor.authorRazzaghi, Hanieh
dc.contributor.authorWieand, Kaliegh
dc.contributor.authorDickinson, Kimberley
dc.contributor.authorBailey, Charles
dc.contributor.otherPEDSnet Data Coordinating Center
dc.date.accessioned2025-11-24T20:47:52Z
dc.date.created2025-11
dc.descriptionThis is a "verse" package that will download all currently available modules and the support function package. Each module is encapsulated in its own package, so they are also available to download in isolation if you would rather not install the full suite. <br><br> ##### Browse individual checks [in PEDSpace](https://hdl.handle.net/20.500.14642/2), or, browse available modules in Github: - **Cohort Fitness** - [Patient Facts](https://ssdqa.github.io/patientfacts): Assesses the availability of patient clinical data per year of follow-up as a factor of visit type - [Patient Event Sequencing](https://ssdqa.github.io/patienteventsequencing): Evaluates the plausibility of the temporal relationship between two clinical events - [Patient Record Consistency](https://ssdqa.github.io/patientrecordconsistency): Checks for consistency within a patient's clinical record to ensure the information is confirmatory and complete - **Variable Testing** - [Expected Variables Present](https://ssdqa.github.io/expectedvariablespresent): Evaluates the presence and distribution of study variables at the patient & row levels - [Quantitative Variable Distribution](https://ssdqa.github.io/quantvariabledistribution): Summarizes the distribution of quantitative study variables (ex: lab test results) - [Categorical Variable Distribution](https://github.com/ssdqa/categoricalvariabledistribution): Summarizes the distribution of categorical study variables, based on what is present in the data (ex: drug dosage units) - **Concept-Set Testing** - [Concept Set Distribution](https://ssdqa.github.io/conceptsetdistribution): Computes the distribution of concepts that make up a given study variable - [Source and Concept Vocabularies](https://ssdqa.github.io/sourceconceptvocabularies): Identifies and summarizes concept mappings for source-to-concept or concept-to-source relationships - **Dataset Fitness** - [Clinical Events & Specialties](https://ssdqa.github.io/clinicalevents.specialties): Assesses the concordance between a clinical event and the associated specialtist providers or care sites - **Cohort Identification** - [Cohort Attrition](https://ssdqa.github.io/cohortattrition): Examine each step of a study's attrition criteria to identify potential irregularities in cohort construction - [Sensitivity to Selection Criteria](https://ssdqa.github.io/sensitivityselectioncriteria): Compare demographics, utilization patterns, and clinical fact makeup of a base cohort definition to alternate cohort definitions
dc.description.abstractThis suite of R packages allows one to investigate multiple facets of data quality and customize analyses based on your study-specific needs. Each module allows up to 8 different analyses in either the OMOP or PCORnet CDM, all aimed at taking a different view of the data while still addressing the same data quality probe. <br><br> ##### [View pkgdown summary here.](https://ssdqa.github.io/squba/)
dc.identifier.urihttps://hdl.handle.net/20.500.14642/1232
dc.publisherPEDSnet
dc.rightsa CC-BY Attribution 4.0 license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectOMOP Data Model
dc.subjectPCORnet Data Model
dc.subjectPEDSnet Data Model
dc.subjectR
dc.titleStudy-Specific Quality, Utility, and Breadth Assessment
dc.title.alternativeSQUBA
dspace.entity.typeCode
local.code.githubhttps://github.com/ssdqa/squba/tree/main
local.code.packagedevtools::install_github('ssdqa/squba')
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