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  1. Maximizing accuracy and precision using individual and grouped exposure assessments.

    Article - En anglais

    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

    Logo du centre Notice produite par :
    Inist-CNRS - Institut de l'Information Scientifique et Technique

    Cote : 96-0438353

    Code Inist : 002B30A01A1. Création : 10/04/1997.