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Related Experiment Videos

Flexible outlier detection in multicenter clinical trials.

Joseph Rigdon¹, Santiago Saldana¹, Sawyer Welden²

¹Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Journal of Clinical and Translational Science

|July 3, 2026

Summary

This summary is machine-generated.

A new algorithm enhances clinical trial data quality by identifying anomalies through univariate and multivariate analyses. This method improves data integrity in large studies, crucial for open-source data resources.

Keywords:

Quality control data repository machine learning open-source Software physical activity

Related Experiment Videos

Area of Science:

Clinical Data Management
Biostatistics
Data Quality Assurance

Background:

Multicenter clinical trials are growing in size and complexity.
Ensuring high-quality data is a critical challenge for data coordinating centers.

Purpose of the Study:

To present a flexible algorithm for detecting data anomalies in clinical trials.
To improve data quality assurance in large-scale research.

Main Methods:

A three-phase algorithm involving univariate and multivariate analyses.
Machine learning is used for multivariate examination of related variables.
Site-level data differences are analyzed using statistical tests and demographic adjustments.

Main Results:

The algorithm identified numerous previously undetected univariate, multivariate, and site-level outliers.
Applied to the MoTrPAC study, it evaluated over 1.9 million observations from 1029 participants.

Conclusions:

Recommends early and repeated application of the algorithm throughout studies.
Aims to maximize data quality, especially for open-source data resources.