Regression calibration of self-reported mobile phone use to optimize quantitative risk estimation in the COSMOS study
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
The Cohort Study of Mobile Phone Use and Health (COSMOS) has repeatedly collected self-reported and operator-recorded data on mobile phone use. Assessing health effects using self-reported information is prone to measurement error, but operator data were available prospectively for only part of the study population and did not cover past mobile phone use. To optimize the available data and reduce bias, we evaluated different statistical approaches for constructing mobile phone exposure histories within COSMOS. We evaluated and compared the performance of four regression calibration (RC) methods (simple, direct, inverse, and generalized additive model for location, shape, and scale), complete-case (CC) analysis and multiple imputation (MI) in a simulation study with a binary health outcome. We used self-reported and operator-recorded mobile phone call data collected at baseline (2007-2012) from participants in Denmark, Finland, the Netherlands, Sweden, and the UK. Parameter estimates obtained using simple, direct, and inverse RC methods were associated with less bias and lower mean squared error than those obtained with CC analysis or MI. We showed that RC methods resulted in more accurate estimation of the relation between mobile phone use and health outcomes, by combining self-reported data with objective operator-recorded data available for a subset of participants.
Date Issued
2024-10-01
Date Acceptance
2023-09-11
Citation
American Journal of Epidemiology, 2024, 193 (10), pp.1482-1493
ISSN
0002-9262
Publisher
Oxford University Press
Start Page
1482
End Page
1493
Journal / Book Title
American Journal of Epidemiology
Volume
193
Issue
10
Copyright Statement
© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
License URL
Identifier
https://academic.oup.com/aje/advance-article/doi/10.1093/aje/kwae039/7671112
Publication Status
Published
Date Publish Online
2024-05-13