Assessing occupational exposure via the one-way random effects model with balanced data

Equivalence testing using existing reference data: An example with genetically modified and conventional crops in animal feeding studies

An equivalence testing method is described to assess the safety of regulated products using relevant data obtained in historical studies with assumedly safe reference products. The method is illustrated using data from a series of animal feeding studies with genetically modified and reference maize varieties. Several criteria for quantifying equivalence are discussed, and study-corrected distribution-wise equivalence is selected as being appropriate for the example case study. An equivalence test is proposed based on a high probability of declaring equivalence in a simplified situation, where there is no between-group variation, where the historical and current studies have the same residual variance, and where the current study is assumed to have a sample size as set by a regulator. The method makes use of generalized fiducial inference methods to integrate uncertainties from both the historical and the current data.

A simple method for assessing occupational exposure via the one-way random effects model

A one-way random effects model is postulated for the log-transformed shift-long personal exposure measurements, where the random effect in the model represents an effect due to the worker. Simple closed-form confidence intervals are proposed for the relevant parameters of interest using the method of variance estimates recovery (MOVER). The performance of the confidence bounds is evaluated and compared with those based on the generalized confidence interval approach. Comparison studies indicate that the proposed MOVER confidence bounds are better than the generalized confidence bounds for the overall mean exposure and an upper percentile of the exposure distribution. The proposed methods are illustrated using a few examples involving industrial hygiene data.

Comparison of means of two lognormal distributions based on samples with multiple detection limits

The problem of comparing the means of two lognormal distributions based on samples with multiple detection limits is considered. Tests and confidence intervals for the ratio of the two means, based on pivotal quantities involving the maximum likelihood estimators, are proposed. The merits of the proposed approaches are evaluated by Monte Carlo simulation. Simulation study indicates that the procedures are satisfactory in terms of coverage probabilities of confidence intervals, and powers of tests. The proposed approach can also be applied to find confidence intervals for the difference between the means of the two lognormal distributions. Illustrative examples with a real data set and with a simulated data set are given.

The symmetric-range accuracy under a one-way random model with balanced or unbalanced data

The symmetric-range accuracy A of a sampler is defined as the fractional range, symmetric about the true concentration, that includes a specified proportion of sampler measurements. In this article, we give an explicit expression for A assuming that the sampler measurements follow a one-way random model so as to capture different components of variability, for example, variabilities among and within different laboratories or variabilities among and within exposed workers. We derive an upper confidence limit for A based on the concept of a 'generalized confidence interval'. A convenient approximation is also provided for computing the upper confidence limit. Both balanced and unbalanced data situations are investigated. Monte Carlo evaluation indicates that the proposed upper confidence limit is satisfactory even for small samples. The statistical procedures are illustrated using an example.

Upper limits for exceedance probabilities under the one-way random effects model

In this article, we propose statistical methods for setting upper limits on (i) the probability that the mean exposure of an individual worker exceeds the occupational exposure limit (OEL) and (ii) the probability that the exposure of a worker exceeds the OEL. The proposed method for (i) is obtained using the generalized variable approach, and the one for (ii) is based on an approximate method for constructing one-sided tolerance limits in the one-way random effects model. Even though tolerance limits can be used to assess the proportion of exposure measurements exceeding the OEL, the upper limits on these probabilities are more informative than tolerance limits. The methods are conceptually as well as computationally simple. Two data sets involving industrial exposure data are used to illustrate the methods.