A forecasting method to reduce estimation bias in self-reported cell phone data

There is ongoing concern that extended exposure to cell phone electromagnetic radiation could be related to an increased risk of negative health effects. Epidemiological studies seek to assess this risk, usually relying on participants' recalled use, but recall is notoriously poor. Our objectives were primarily to produce a forecast method, for use by such studies, to reduce estimation bias in the recalled extent of cell phone use. The method we developed, using Bayes' rule, is modelled with data we collected in a cross-sectional cluster survey exploring cell phone user-habits among New Zealand adolescents. Participants recalled their recent extent of SMS-texting and retrieved from their provider the current month's actual use-to-date. Actual use was taken as the gold standard in the analyses. Estimation bias arose from a large random error, as observed in all cell phone validation studies. We demonstrate that this seriously exaggerates upper-end forecasts of use when used in regression models. This means that calculations using a regression model will lead to underestimation of heavy-users' relative risk. Our Bayesian method substantially reduces estimation bias. In cases where other studies' data conforms to our method's requirements, application should reduce estimation bias, leading to a more accurate relative risk calculation for mid-to-heavy users.