Maximizing accuracy and precision using individual and grouped exposure assessments.

OBJECTIVES: Random errors in exposure data were explored to determine their effect on exposure-response relationships using individual, grouped, or combined (grouped and individual) exposure assessment methods.
METHODS: Monte Carlo simulations were conducted by generating small "studies" of one hundred subjects divided into four exposure groups. Observed exposure data were generated for each individual using assumed inter- and intraindividual variances and a lognormal distribution. The data were used to calculate the following three estimates of exposure: an individual mean, a group mean, and a hybrid estimate using the James-Stein shrinkage estimator. The exposure estimates were regressed on generated (continuous) "health outcomes," and the regression results were stored and analyzed.
RESULTS: Random errors in exposure data resulted in attenuation of the exposure-response relationship when the individual estimates were used, especially when the within-subject variability was high. The attenuation was substantially controlled by the group mean estimate, however, at a cost of decreased precision. The hybrid estimator simultaneously controlled both bias and imprecision in the observed exposure-response function.
CONCLUSIONS: While estimates of exposure based on individual means may result in attenuation of the exposure-response relationship, grouped estimates may control bias while decreasing precision. Combining individual and group estimates can simultaneously control both types of error. However, further research is required to determine how robust these findings are to different error structures, grouping strategies, exposure-response models, and exposure assessment methods.