Frome E L, Smith M H, Littlefield L G, Neubert R L, Colyer S P
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Tennessee 37831-6367, USA.
Environ Health Perspect. 1996 Oct;104 Suppl 5(Suppl 5):957-68. doi: 10.1289/ehp.96104s5957.
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 (approximately 7%). A long-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. 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 Sls using combined data from the nonexposed 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.
血液铍淋巴细胞增殖试验（BeLPT）是标准淋巴细胞增殖试验的一种改良方法，用于识别可能患有慢性铍病的人。BeLPT试验结果解释中的一个主要问题是重复孔计数中的异常数据值（约7%）。使用长线性回归模型来描述每组铍暴露条件下的预期孔计数，孔计数的方差与预期计数的平方成正比。使用两种抗异常值回归方法来估计刺激指数（SI）和变异系数。第一种方法是对孔计数的对数使用最小绝对值（LAV）作为估计方法；第二种方法使用最大拟似然估计的抗异常值回归版本。这些抗异常值方法的一个主要优点是无需识别和删除异常值。将这两种用于BeLPT数据统计分析的新方法和当前的异常值剔除方法应用于173次BeLPT检测。我们强烈推荐LAV方法用于BeLPT的常规分析。在试图识别铍超敏个体时，异常值很重要，因为这些个体通常具有较大的正SI值。提出了一种使用非暴露组和铍作业工人的组合数据来识别大SI值的新方法。对数（SI）用高斯分布描述，其位置和尺度参数使用抗异常值方法估计。将该方法应用于测试数据，并将结果与当前方法获得的结果进行比较。
