Prospective adverse event risk evaluation in clinical trials
| Autor: | Diego Martinez, Abhishake Kundu, Felipe Feijoo, Manuel Hermosilla, Timothy I. Matis |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Models Statistical business.industry Medicine (miscellaneous) Retrospective cohort study Health informatics Health administration Clinical trial Treatment Outcome Informed consent Risk Factors General Health Professions medicine Humans Prospective Studies Intensive care medicine Adverse effect business Predictive modelling Rank correlation Retrospective Studies |
| Zdroj: | Health care management science. 25(1) |
| ISSN: | 1386-9620 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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