Detecting irregularities in randomized controlled trials using machine learning.

Journal: Clinical trials (London, England)
PMID: 39587801

Abstract

BACKGROUND: Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the trial are analyzed and disseminated. Machine learning algorithms have shown promise for identifying center-level irregularities in multi-center clinical trials with minimal human intervention. We aimed to characterize the form-level data irregularities in several historical clinical trials and evaluate the ability of a machine learning-based outlier detection algorithm to identify them.

Authors

  • Walter Nelson
    Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.
  • Jeremy Petch
    Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.
  • Jonathan Ranisau
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.
  • Robin Zhao
    Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice at Weill Cornell Medicine, New York City, New York, USA.
  • Kumar Balasubramanian
    Population Health Research Institute, McMaster University, Canada.
  • Shrikant I Bangdiwala
    Population Health Research Institute, McMaster University, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University, Canada. Electronic address: shrikant.bangdiwala@phri.ca.

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