title: Improved non-negative estimation of variance components for exposure assessment.
abstract: Hygiene surveys of pollutants exposure data can be analyzed by analysis of variance (ANOVA) model with a random worker effect. Typically, workers are classified into homogeneous exposure groups, so it is very common to obtain a zero or negative ANOVA estimate of the between-worker variance (sigma2B). Negative estimates are not sensible and also pose problems for estimating the probability (theta) that in a job group, a randomly selected worker's mean exposure exceeds the occupational exposure standard. Therefore, it was suggested by Rappaport et al. to replace a non-positive estimate with an approximate one-sided 60% upper confidence bound. This article develops an alternative estimator, based on the upper tolerance interval suggested by Wang and Iyer. We compared the performance of the two methods using real data and simulations with respect to estimating both the between-worker variance and the probability of overexposure in balanced designs. We found that the method of Rappaport et al. has three main disadvantages: (i) the estimated sigma2B remains negative for some data sets; (ii) the estimator performs poorly in estimating sigma2B and theta with two repeated measures per worker and when true sigma2B is quite small, which are quite common situations when studying exposure; (iii) the estimator can be extremely sensitive to small changes in the data. Our alternative estimator offers a solution to these problems.
authors.affiliation: The Department of Epidemiology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. cperetz@post.tau.ac.il
authors.forename: C
authors.initials: C
authors.lastname: Peretz
authors.forename: D M
authors.initials: DM
authors.lastname: Steinberg
mesh_terms: D015982:Bias; D004785:Environmental Pollutants / Q000032:analysis*; D006801:Humans; D008962:Models, Theoretical; D016273:Occupational Exposure / Q000706:statistics & numerical data*
chemical_list: D004785:Environmental Pollutants