Quantitative Variable Distributions: Single Site, Anomaly Detection, Cross-Sectional Analysis


Created

2025-07-30

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Domain

Category

Parameters

Publisher

PEDSnet

Abstract

This check provides raw data and visualizations to aid a user in evaluating whether the distribution of quantitative variables aligns with clinical expectations. It can summarize the distribution of a quantitative variable (like lab result values) or patient counts (like number of patients with an outpatient visit).

Data Requirements

cohort , qvd_value_file , omop_or_pcornet , multi_or_single_site , anomaly_or_exploratory , age_groups , sd_threshold

Probe

Clinical Assessment

Access Package

# install.packages(""devtools"") devtools::install_github('ssdqa/https://github.com/ssdqa/quantvariabledistribution')

Visualization Output

This check outputs a bar graph visualizing the proportion of values that are considered outliers based on the number of standard devations they fall from the mean. The outliers are stratified by upper and lower outlier types so the user can differentiate between outliers above and below the mean.

Raw Output

This check produces a raw data output containing 8 columns:

Column Data Type Definition
site character the name of the site being targeted OR “combined” if multiple sites were provided
value_type character the type of value being measured
outlier_type character a string indicating whether the outlier is in the upper (positive) or lower (negative) direction
total_vals numeric the total number of values
sd_threshold numeric the number of standard deviations a value should fall in either direction of the mean to be considered an outlier
n_outlier numeric the number of outlying values
prop_outlier numeric the proportion of outlying values
output_function character a string indicating the type of visualization that should be generated by qvd_output

Affiliation(s)

PEDSnet Data Coordinating Center

Funder(s)

This research was made possible through the generous support of Patient-Centered Outcomes Research Institute. The statements presented in this work are solely the responsibility of the author(s) and do not necessarily represent the views of PCORI, its Board of Governors, or its Methodology Committee.

Provenance

Description

Development Code

https://github.com/ssdqa/quantvariabledistribution

Clinical Subjects Headings

Related Data Quality Result

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Quantitative Variable Distribtion Study Results I: PRESERVE
Created:2025-04-08Affiliation:PEDSnet Data Coordinating Center
The results of a Quantitative Variable Distribution check using the Single Site, Anomaly Detection, Cross-Sectional parameters. This check evaluates upper and lower outliers, 2 or more standard deviations away from the mean, in blood pressure, eGFR, and urine protein distributions

Related Person

Related Code

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Study-Specific Quality, Utility, and Breadth Assessment
Created:2025-11Affiliation:PEDSnet Data Coordinating Center
This 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.

##### [View pkgdown summary here.](https://ssdqa.github.io/squba/)

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Creative Commons license

a CC-BY 4.0 Attribution license.
Except where otherwised noted, this item's license is described as a CC-BY 4.0 Attribution license.

Cite this Data Quality Check

PEDSnet Data Coordinating Center., Wieand, K., & Dickinson, K. (2025, July). Quantitative Variable Distributions: Single Site, Anomaly Detection, Cross-Sectional Analysis. [D Q Check]. PEDSpace Knowledge Bank. https://doi.org/10.24373/pdsp-474