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

Abstract

AbstractThe Cohort Study of Mobile Phone Use and Health (COSMOS) study has repeatedly collected both self-reported and operator-recorded data on mobile phone use. Assessing health effects using self-reported information only 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 complete case-analysis, different regression calibration methods, 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 UK. Parameter estimates obtained using regression calibration methods were associated with less bias and lower mean squared error than those obtained with complete-case analysis or multiple-imputation. Our simulation study showed that regression calibration 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 the participants.