Patient-Facts: Multi-Site, Anomaly Detection, Longitudinal Analysis


Created

2024-06-05

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Domain

Category

Parameters

Publisher

PEDSnet

Abstract

This check assesses how much clinical data is available for patients accross time. It provides a high level summary of anomalous/outlier clinical data for multiple sites. The number of clinical events per year of follow-up for each patient in a cohort is computed and stratified by visit type.

Data Requirements

cohort , study_name , patient_level_tbl , visit_types , omop_or_pcornet , multi_or_single_site , time , time_span , time_period , p_value , age_groups , anomaly_or_exploratory , domain_tbl , visit_type_table

Probe

Clinical Assessment

Access Package

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

Visualization Output

This check outputs three visualizations to display the Euclidean distance between two time series: the smoothed (Loess) proportion of a user-selected variable for a given site, and the average proportion of all sites. Two line graphs (one smoother, one raw) represent the proportion of the variable at each site over time. Sites are differentiated by color, and a thick red line represente the All Site Average. A circular bar graph displays the Euclidean distance from the all-site mean where the color represents the average Loess proportion over time.

Raw Output

The raw data output of this check produces ten columns of data:

Column Data Type Definition
site character the name of the site being targeted
time_start date the start of the time period being examined
visit_type character string indicating the visit type
domain character string indicating the domain
prop_pts_fact numeric the proportion of patients with the domain of interest out of all patients with a visit of the visit type of interest during the time period
mean_allsiteprop numeric the average patient proportion across sites
median numeric the median patient proportion across sites
date_numeric numeric the numeric equivalent of time_start
site_loess numeric the patient proportion with Loess regression applied
dist_eucl_mean numeric the Euclidean distance of site_loess from mean_allsiteprop

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/patientfacts

Clinical Subjects Headings

Related Data Quality Result

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Patient Facts Study Results Part II: SSDQA Comparison
Created:2025-09Affiliation:PEDSnet Data Coordinating Center
The results of a Patient Facts check using the Multi-Site, Anomaly Detection, Longitudinal parameters. This check investigates patients with facts in relevant domains across the study period to identify longitudinal trends.
Thumbnail Image
Patient Facts Study Results Part III: SSDQA Comparison
Created:2025-10Affiliation:PEDSnet Data Coordinating Center
The results of a Patient Facts check using the Multi-Site, Anomaly Detection, Longitudinal parameters. This check investigates longitudinal trends in key domains, with a focus on important study variables like ANC, MCV, & hemoglobin labs and hydroxyurea.

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

Cite this Data Quality Check

PEDSnet Data Coordinating Center. (2024, June). Patient-Facts: Multi-Site, Anomaly Detection, Longitudinal Analysis. [D Q Check]. PEDSpace Knowledge Bank. https://doi.org/10.24373/pdsp-416