Statistical methods for the blood beryllium lymphocyte proliferation test.
Statistical methods for the blood beryllium lymphocyte proliferation test.
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DOI：
10.1289/ehp.96104s5957
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发表时间：
1996-10
影响因子：
10.4
通讯作者：
Colyer SP
中科院分区：
文献类型：
--
作者：
Frome EL;Smith MH;Littlefield LG;Neubert RL;Colyer SP
血铍淋巴细胞增殖试验(BeLPT)是对标准淋巴细胞增殖试验的改进，该试验用于识别可能患有慢性铍病的人。BeLPT测试结果解释中的一个主要问题是重复井数中的异常数据值(约7%)。用长线性回归模型来描述每组铍暴露条件下的期望井数，并且井数的方差与期望井数的平方成正比。采用两种抗异常值的回归方法来估计刺激指数和变异系数。第一种方法使用井数记录上的最小绝对值(LAV)作为估计方法；第二种方法使用抵抗回归版本的最大准似然估计。这些抵抗方法的一个主要优点是，它们不需要识别和删除离群值。这两种新的BeLPT数据统计分析方法和当前的离群值剔除方法被应用于173个BeLPT分析。我们强烈推荐将LAV方法用于BeLPT的常规分析。在试图识别铍过敏患者时，离群值非常重要，因为这些患者通常具有较大的正SI值。提出了一种利用非暴露人群和铍作业人员的联合数据识别大型SLS的新方法。对数(SI)S用正态分布描述，位置和尺度参数用阻抗法估计。将该方法应用于试验数据，并与现有方法的结果进行了比较。
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.