Assessment of occupational exposure using one-sided tolerance limits

Tuggle, R.M.

doi:10.1202/0002-8894(1982)043<0338:aooeuo>2.3.co;2

Cited by 4 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An area is generally considered to be well controlled to a specified level when the exceedance fraction for that level is (5%. 12 Exceedances were not calculated for sample sizes ,15. For all analyses, p(0.05 was considered significant.…”

Section: Airborne Beryllium Comparisonsmentioning

confidence: 99%

Enhanced preventive programme at a beryllium oxide ceramics facility reduces beryllium sensitisation among new workers

et al. 2006

View full text Add to dashboard Cite

show abstract

Section: Airborne Beryllium Comparisonsmentioning

confidence: 99%

Enhanced preventive programme at a beryllium oxide ceramics facility reduces beryllium sensitisation among new workers

et al. 2006

View full text Add to dashboard Cite

show abstract

“…A perhaps more common way to address the problem of compliance to a given TLV is in terms of tolerance limits as described, for instance, by Tuggle (1982), which are the limits of a confidence interval for a percentile of the log-normal distribution. A perhaps more common way to address the problem of compliance to a given TLV is in terms of tolerance limits as described, for instance, by Tuggle (1982), which are the limits of a confidence interval for a percentile of the log-normal distribution.…”

Section: Discussionmentioning

confidence: 99%

Confidence Intervals for Probabilities of Exceeding Threshold Limits With Censored Log-Normal Data

Wild

Hordan²,

Leplay

et al. 1996

Environmetrics

View full text Add to dashboard Cite

In enforcing safety regulations in the context of occupational health, the question of the probability of exceeding the so‐called threshold limit value (TLV) in homogeneous exposure groups (HEG) arises. Because data are usually few, a simple non‐parametric approach based on numbers of exceedances over the TLV cannot be usefully adopted. However, the distribution of airborne concentrations in the exposures within HEGs is often compatible with a log‐normal distribution. Therefore inference based on this distribution may be adopted. There remain two problems for which there has been no satisfactory solution: the treatment of left censoring due to the detection limits of the exposure determinations, and the absence of a confidence interval (CI) for the probability of exceedance. In the non‐censored case we present an exact confidence interval based on the non‐central T‐distribution and two Monte Carlo solutions based on the bootstrap and on the Gibbs sampler. The Monte Carlo methods are extended in various ways to treat the censored case. A large scale simulation study shows that the Gibbs Sampling CIs has coverage close to nominal in nearly all situations. The parametric bootstrap, after bias correction, performs nearly as well. We also present some extensions of the Gibbs sampling method to the analysis of fibre exposure data, and to the analysis of the effect of finite precision and measurement error. A worked example is presented.

show abstract

“…In this paper we have concentrated on obtaining a confidence interval for the probability of exceeding a TLV. A perhaps more common way to address the problem of compliance to a given TLV is in terms of tolerance limits as described, for instance, by Tuggle (1982), which are the limits of a confidence interval for a percentile of the log-normal distribution. In this paper Tuggle refers to published tables based on the non-central T-distribution and the derivation of these confidence limits follows essentially the same lines as in our paper (Section 2.2).…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, even if all of a number of measures are below the TLV, there may still be a substantial probability that the next measure would exceed it. In order to cope with these difficulties, the results are now usually expressed in terms of probabiility of exceeding the TLV (Tuggle 1982;Hawkins et al 1991;European Norm Project 689 1992) under the assumption that the data are log-normally distributed. This model, the so-called Larsen model (Larsen 1969) is based on many real data sets which are compatible with the log-normal distribution assumption and on some theoretical considerations based on diffusion properties of airborne particles.…”

Section: Introductionmentioning

confidence: 99%

Confidence Intervals for Probabilities of Exceeding Threshold Limits With Censored Log‐normal Data

Wild

HORDAN²,

Leplay

et al. 1996

Environmetrics

View full text Add to dashboard Cite

SUMMARYIn enforcing safety regulations in the context of occupational health, the question of the probability of exceeding the so-called threshold limit value (TLV) in homogeneous exposure groups (HEG) arises. Because data are usually few, a simple non-parametric approach based on numbers of exceedances over the TLV cannot be usefully adopted. However, the distribution of airborne concentrations in the exposures within HEGs is often compatible with a log-normal distribution. Therefore inference based on this distribution may be adopted. There remain two problems for which there has been no satisfactory solution: the treatment of left censoring due to the detection limits of the exposure determinations, and the absence of a confidence interval (CI) for the probability of exceedance. In the non-censored case we present an exact confidence interval based on the non-central T-distribution and two Monte Carlo solutions based on the bootstrap and on the Gibbs sampler. The Monte Carlo methods are extended in various ways to treat the censored case. A large scale simulation study shows that the Gibbs Sampling CIS has coverage close to nominal in nearly all situations. The parametric bootstrap, after bias correction, performs nearly as well. We also present some extensions of the Gibbs sampling method to the analysis of fibre exposure data, and to the analysis of the effect of finite precision and measurement error. A worked example is presented.

show abstract

Assessment of occupational exposure using one-sided tolerance limits

Cited by 4 publications

References 0 publications

Enhanced preventive programme at a beryllium oxide ceramics facility reduces beryllium sensitisation among new workers

Enhanced preventive programme at a beryllium oxide ceramics facility reduces beryllium sensitisation among new workers

Confidence Intervals for Probabilities of Exceeding Threshold Limits With Censored Log-Normal Data

Confidence Intervals for Probabilities of Exceeding Threshold Limits With Censored Log‐normal Data

Contact Info

Product

Resources

About