Open AccessA data-driven approach to quality risk management
Author(s) -
Demissie Alemayehu,
José Alvir,
Marcia Levenstein,
David F. Nickerson
Publication year - 2013
Publication title -
perspectives in clinical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.649
H-Index - 8
eISSN - 2229-5488
pISSN - 2229-3485
DOI - 10.4103/2229-3485.120171
Subject(s) - wilcoxon signed rank test , quality (philosophy) , risk analysis (engineering) , clinical trial , logistic regression , computer science , data quality , identification (biology) , medicine , data mining , operations management , engineering , machine learning , metric (unit) , philosophy , epistemology , pathology , mann–whitney u test , botany , biology
An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials.