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Prospective adverse event risk evaluation in clinical trials

Author

Listed:
  • 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)

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

Suggested Citation

  • Abhishake Kundu & Felipe Feijoo & Diego A. Martinez & Manuel Hermosilla & Timothy Matis, 2022. "Prospective adverse event risk evaluation in clinical trials," Health Care Management Science, Springer, vol. 25(1), pages 89-99, March.
    • Handle: RePEc:kap:hcarem:v:25:y:2022:i:1:d:10.1007_s10729-021-09584-y
    DOI: 10.1007/s10729-021-09584-y
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