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Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study

Crossref DOI link: https://doi.org/10.1186/s12874-025-02620-3

Published Online: 2025-07-14

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Carnes, Nate C.

Kolaja, Claire A.

Lewis, Crystal L.

Castañeda, Sheila F.

Rull, Rudolph P.

,

Bukowinski, Ania

Carey, Felicia

Geronimo-Hara, Toni

Hall, Clinton

Harbertson, Judy

Ignacio, David

Jacobson, Isabel G.

LeardMann, Cynthia A.

Perez, Vanessa

Piega, Aprilyn

Rivera, Anna

Salvatier, Rosa

Sharifian, Neika

Speigle, Steven

Trone, Daniel

Villalobos, Javier

Zhu, Katie
Funding

Funding for this research was provided by:

Defense Health Program, U.S. Department of Veterans Affairs Office of Research and Development (60002, 60002)
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Text and Data Mining valid from 2025-07-14

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Article History

Received: 17 December 2024

Accepted: 20 June 2025

First Online: 14 July 2025

Declarations

:

: This study adhered to the Declaration of Helsinki. The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable federal regulations governing the protection of human subjects. Research data were derived from approved Naval Health Research Center Institutional Review Board protocol number NHRC.2000.0007. All participants provided written informed consent at study enrollment.

: Not applicable.

: The authors declare no competing interests.

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