Programmaleider Zorgdata en het Lerend Zorgsysteem; bijzonder hoogleraar 'Transparantie in de zorg vanuit patiëntenperspectief', Tranzo, Tilburg University
Publicatie
Publicatie datum
Regression calibration of self-reported mobile phone use to optimize quantitative risk estimation in the COSMOS study.
Reedijk, M., Portengen, L., Auvinen, A., Kojo, K., Heinavaara, S., Feychting, M., Tettamanti, G., Hillert, L., Elliot, P., Toledano, M.B., Smith, R.B., Heller, J., Schüz, J., Deltour, I., Poulsen, A.H., Johansen, C., Verheij, R., Peeters, P., Rookus, M., Traini, E., Huss, A., Kromhout, H., Vermeulen, R. Regression calibration of self-reported mobile phone use to optimize quantitative risk estimation in the COSMOS study. American Journal of Epidemiology: 2024
Lees online
Objective
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.
Methods
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.
Findings
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.
Conclusion
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.
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.
Methods
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.
Findings
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.
Conclusion
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.
Objective
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.
Methods
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.
Findings
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.
Conclusion
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.
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.
Methods
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.
Findings
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.
Conclusion
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.