Precision recruitment for high-risk participants in a COVID-19 cohort study.
CDC, Centers for Disease Control and Prevention
COVID-19
Clinical trials
GAMs, generalized additive models
Risk modeling
Journal
Contemporary clinical trials communications
ISSN: 2451-8654
Titre abrégé: Contemp Clin Trials Commun
Pays: Netherlands
ID NLM: 101671157
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
28
11
2022
revised:
07
03
2023
accepted:
10
03
2023
entrez:
20
3
2023
pubmed:
21
3
2023
medline:
21
3
2023
Statut:
ppublish
Résumé
Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals. We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials. When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts. This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.
Sections du résumé
Background
UNASSIGNED
Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals.
Methods
UNASSIGNED
We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials.
Results
UNASSIGNED
When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts.
Conclusion
UNASSIGNED
This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.
Identifiants
pubmed: 36938318
doi: 10.1016/j.conctc.2023.101113
pii: S2451-8654(23)00059-5
pmc: PMC10008035
doi:
Types de publication
Journal Article
Langues
eng
Pagination
101113Informations de copyright
© 2023 Published by Elsevier Inc.
Déclaration de conflit d'intérêts
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:AMM, EC, AS, ER, and LF are employees of Evidation Health, Inc., developers of the Evidation health and research platform.
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