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. 2024 Oct 7;193(10):1482-1493.
doi: 10.1093/aje/kwae039.

Regression calibration of self-reported mobile phone use to optimize quantitative risk estimation in the COSMOS study

Marije Reedijk  1   2 , Lützen Portengen  1 , Anssi Auvinen  3   4 , Katja Kojo  4 , Sirpa Heinävaara  5 , Maria Feychting  6 , Giorgio Tettamanti  6 , Lena Hillert  6 , Paul Elliott  7   8   9   10 , Mireille B Toledano  7   8   9   10 , Rachel B Smith  7   8   9   10 , Joël Heller  7   8   9 , Joachim Schüz  11 , Isabelle Deltour  11 , Aslak Harbo Poulsen  12 , Christoffer Johansen  12   13 , Robert Verheij  14 , Petra Peeters  2 , Matti Rookus  15 , Eugenio Traini  1 , Anke Huss  1 , Hans Kromhout  1 , Roel Vermeulen  1   2   7   8 ; COSMOS Study Group
Affiliations

Regression calibration of self-reported mobile phone use to optimize quantitative risk estimation in the COSMOS study

Marije Reedijk et al. Am J Epidemiol. .

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 4 regression calibration (RC) methods (simple, direct, inverse, and generalized additive model for location, shape, and scale), complete-case analysis, and multiple imputation 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 United Kingdom. Parameter estimates obtained using simple, direct, and inverse RC methods were associated with less bias and lower mean squared error than those obtained with complete-case analysis or multiple imputation. We showed that RC methods resulted in more accurate estimation of the relationship between mobile phone use and health outcomes by combining self-reported data with objective operator-recorded data available for a subset of participants.

Keywords: cohort analysis; exposure assessment; health outcomes; measurement error; mobile phone use; regression calibration.

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Conflict of interest statement

M.F. was vice chairman (2012-2020) of the International Commission on Non-Ionizing Radiation Protection, an independent body setting guidelines for non-ionizing radiation protection. She has served as advisor to a number of national and international public advisory and research steering groups concerning the potential health effects of exposure to non-ionizing radiation, currently for the World Health Organization (WHO). H.K. was the chair of the Committee on Electromagnetic Fields of the Health Council of The Netherlands till 2022. He currently is a member of the WHO Task Group for the Environmental Health Criteria Monograph on RF-EMF. A.H. is a member of the International Commission on Non-Ionizing Radiation Protection since 2020, and of the Committee on Electromagnetic Fields of the Health Council of The Netherlands, and chairs the Swedish Radiation Safety Authority’s (SSM) Scientific Council on Electromagnetic Fields since 2020. A.A. currently is a member of the WHO Task Group for the Environmental Health Criteria Monograph on RF-EMF. M.B.T. is currently a member of the WHO groups tasked with systematic review of evidence on non-ionizing radiation and health, feeding into the Environmental Health Criteria Monograph on RF-EMF. All other authors declare they have no competing financial interests.

Figures

Figure 1
Figure 1
Roadmap to the main results from this paper, using data from the Cohort Study of Mobile Phone Use and Health (COSMOS), multiple countries, 2007-2012. This includes evaluation of model fit and estimated determinant effects for regression calibration (RC) models fitted to the available cohort data and results of the simulation study (including sensitivity analyses). GAMLSS, generalized additive model for location, shape, and scale; MSE, mean squared error; RC, regression calibration; SE, standard error.
Figure 2
Figure 2
Outgoing operator-recorded mobile phone use according to country and categories of self-reported use, using data from the Cohort Study of Mobile Phone Use and Health (COSMOS), multiple countries, 2007-2012.
Figure 3
Figure 3
Continues.
Figure 3
Figure 3
Continues.
Figure 3
Figure 3
Observed versus predicted duration of outgoing calls according to country for the direct (left column) and inverse (right column) regression calibration (RC) approaches, using data from the Cohort Study of Mobile Phone Use and Health (COSMOS), multiple countries, 2007-2012. A) Denmark; B) Finland; C) The Netherlands; D) Sweden; E) United Kingdom. The regression line was estimated allowing for a nonlinear relationship between observed and predicted values and allowing the (residual) variance in observed durations to depend on predicted duration using penalized splines (P-splines) as implemented in the generalized additive model for location, shape, and scale software. Note the different horizontal and vertical scales for the Netherlands.
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