Measurement Error in Exposure Assessment: An Error Model and its Impact on Studies on Lung Cancer and Residential Radon Exposure in Germany

Case-control studies on lung cancer and residential radon exposure had been conducted in Germany. Relative risk estimates from primary analysis were now subject to accounting for uncertainties in radon exposure and in the most potent confounder smoking. The regression calibration method and an approximate maximum likelihood method were applied. The differentiation between classical error (from assessing radon exposure or packyears) or Berkson error (from using radon exposure instead of alpha dose, from using packyears instead of inhaled dose of smoking carcinogens) was of major importance in this analysis. Estimates of relative lung cancer risk due to radon exposure were found to be higher after accounting for multiplicative classical error in radon exposure and packyears. Outliers in the data strongly influence risk estimates, but their impact is reduced, if classical error is accounted for. In one study, the influence of one outlier explained the particularly large risk-increasing impact of error correction. But also residual confounding due to adjusting for imprecisely measured packyears deflated the risk estimate in this study. It is interesting that the small correlation between radon exposure and packyears had this notable effect. On the other hand, classical errors in packyears had no large impact in the radon-prone study areas. Further, Berkson error did not induce substantial bias on the radon risk estimates, but possibly decreased the power to detect existing effects and inflated the confidence intervals. It was concluded that such an analysis was extremely valuable to understand the impact of uncertainties in the risk factor of primary interest on the risk estimate under study and the potential for residual confounding by assessment errors in the smoking variable. Note that assuming some error in the risk factors is more realistic than assuming no error. With regard to study design, study regions with no correlation between the variable of primary interest and potential confounders are preferable. However, the exact magnitude of the error could not be estimated based on the available data. Further investigations regarding residual confounding due to model mis-specification and latent smoking-related variables are necessary to grasp the full dimension of an important issue in epidemiology, i.e. the role of the outstanding confounder smoking for estimating small risks.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Ludwig-Maximilians-Universität

Year

2002

Author dc:creator

• Heid, Iris

Repository record source_url

https://edoc.ub.uni-muenchen.de/522/

OAI identifier oai:identifier

oai:edoc.ub.uni-muenchen.de:522