Prospective adverse event risk evaluation in clinical trials

Abhishake Kundu, Felipe Feijoo (), Diego A. Martinez, Manuel Hermosilla and Timothy Matis
Additional contact information
Abhishake Kundu: Texas Tech University
Felipe Feijoo: Pontificia Universidad Católica de Valparaíso
Diego A. Martinez: Johns Hopkins University School of Medicine
Manuel Hermosilla: The Johns Hopkins Carey Business School
Timothy Matis: Texas Tech University

Health Care Management Science, 2022, vol. 25, issue 1, No 5, 89-99

Abstract: Abstract Proactive and objective regulatory risk management of ongoing clinical trials is limited, especially when it involves the safety of the trial. We seek to prospectively evaluate the risk of facing adverse outcomes from standardized and routinely collected protocol data. We conducted a retrospective cohort study of 2860 Phase 2 and Phase 3 trials that were started and completed between 1993 and 2017 and documented in ClinicalTrials.gov. Adverse outcomes considered in our work include Serious or Non-Serious as per the ClinicalTrials.gov definition. Random-forest-based prediction models were created to determine a trial’s risk of adverse outcomes based on protocol data that is available before the start of a trial enrollment. A trial’s risk is defined by dichotomic (classification) and continuous (log-odds) risk scores. The classification-based prediction models had an area under the curve (AUC) ranging from 0.865 to 0.971 and the continuous-score based models indicate a rank correlation of 0.6–0.66 (with p-values

Keywords: Clinical trials; Adverse event risk; Machine learning; Drug regulation (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10729-021-09584-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:hcarem:v:25:y:2022:i:1:d:10.1007_s10729-021-09584-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10729

DOI: 10.1007/s10729-021-09584-y

Access Statistics for this article

Health Care Management Science is currently edited by Yasar Ozcan

More articles in Health Care Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().