Measurement error (ME) can lead to bias in the analysis of epidemiologic studies. Here a simulation study is described that is based on data from the French Uranium Miners’ Cohort and that was conducted to assess the effect of ME on the estimated excess relative risk (ERR) of lung cancer death associated with radon exposure. Starting from a scenario without any ME, data were generated containing successively Berkson or classical ME depending on time periods, to reflect changes in the measurement of exposure to radon (222Rn) and its decay products over time in this cohort. Results indicate that ME attenuated the level of association with radon exposure, with a negative bias percentage on the order of 60% on the ERR estimate. Sensitivity analyses showed the consequences of specific ME characteristics (type, size, structure, and distribution) on the ERR estimates. In the future, it appears important to correct for ME upon analyzing cohorts such as this one to decrease bias in estimates of the ERR of adverse events associated with exposure to ionizing radiation.