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The blood beryllium lymphocyte proliferation test (BeLPT) is a
modification of the standard lymphocyte proliferation test that is
used to identify persons who may have chronic beryllium disease. A
major problem in the interpretation of BeLPT test results is
outlying data values among the replicate well counts ( about
7% ). A log-linear regression model is used to describe the
expected well counts for each set of Be exposure conditions, and the
variance of the well counts is proportional to the square of the
expected count. Two outlier resistant regression methods are used to
estimate stimulation indices (SIs) and the coefficient of variation.
The first approach uses least absolute values (LAV) on the log of
the well counts as a method for estimation; the second approach uses
a resistant regression version of maximum quasi-likelihood
estimation. A major advantage of these resistant methods is
that they make it unnecessary to identify and delete outliers.
These two new methods for the statistical analysis of the BeLPT data
and the current ``outlier rejection'' method are applied to 173
BeLPT assays. We strongly recommend the LAV method for routine
analysis of the BeLPT.
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Outliers are important when trying to identify individuals with
beryllium hypersensitivity, since these individuals typically have large
positive SI values. A new method for identifying large
SIs using combined data from the not exposed group and the beryllium
workers is proposed. The log(SI)s are described with a Gaussian
distribution with location and scale parameters estimated using
resistant methods. This approach is applied to the
test data
and results are compared with those obtained from the current method.
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Research was supported by the
Offices of Occupational Medicine and Epidemiology and Health
Surveillance, Environment, Safety and Health, U. S. Department of
Energy under contract DE-AC05-96OR22464 with Lockheed Martin
Energy Research Corp., and DA-AC05-76OR00033 with Oak Ridge
Associated Universities.
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*Last Modified July 1, 1997* FromeEL@ornl.gov
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