1. ¹The Department of Epidemiology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
2. ²The Department of Statistics and Operations Research, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel

Correspondence: Chava Peretz, The Department of Epidemiology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel. Tel.: +972-3-640-9867. Fax: +972-3-641-0555. E-mail: cperetz@post.tau.ac.il

Received 24 July 2001.

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 (_B²). Negative estimates are not sensible and also pose problems for estimating the probability () 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 _B² remains negative for some data sets; (ii) the estimator performs poorly in estimating _B² and with two repeated measures per worker and when true _B² 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.