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. (...)
Mots-clés Pascal : Biais méthodologique, Exposition, Evaluation, Méthodologie, Epidémiologie, Précision
Mots-clés Pascal anglais : Methodological bias, Exposure, Evaluation, Methodology, Epidemiology, Accuracy
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 96-0438353
Code Inist : 002B30A01A1. Création : 10/04/1997.