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A statistically defined endpoint titer determination method for immunoassays.

机译:统计确定的用于免疫测定的终点滴度测定方法。

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摘要

Results of immunoassays for which no positive standards are available are often expressed as endpoint titers. The endpoint titer is defined as the reciprocal of the highest analyte dilution that gives a reading above the cutoff. Unfortunately, there is no generally accepted rule for the determination of these cutoff values. In enzyme-linked immunosorbent assays (ELISA) a value two or three times the mean background or negative control reading is sometimes used. Other investigators set the cutoff arbitrarily at a certain absorbance value. These procedures do not provide statistically meaningful information about the risk of overtitration or false low titers. We have solved this problem by devising a practical method for establishing a statistically valid cutoff. The procedure involves calculating the upper prediction limit using the Student t-distribution. The mathematical formula which defines the upper prediction limit is expressed as the standard deviation multiplied by a factor which is based on the number of negative controls and the confidence level (1 - alpha). Appropriate factors are provided for 2 to 30 negative controls and for confidence levels ranging from 95% to 99.9%. Our new method is more reliable than other nonstatistical procedures yet does not require sophisticated computation. It can be applied to a variety of immunoassays provided that negative controls are available.
机译:没有阳性标准的免疫分析结果通常表示为终点滴度。终点滴度定义为最高分析物稀释度的倒数,即给出高于临界值的读数。不幸的是,没有确定这些临界值的普遍接受的规则。在酶联免疫吸附测定(ELISA)中,有时会使用平均背景或阴性对照读数的两倍或三倍的值。其他研究者将截止值任意设置为某个吸光度值。这些程序没有提供有关过度滴定或错误的低滴度风险的统计上有意义的信息。我们通过设计一种建立统计上有效的临界值的实用方法解决了这个问题。该过程涉及使用学生t分布来计算预测上限。定义预测上限的数学公式表示为标准偏差乘以基于阴性对照的数量和置信度(1-α)的因子。为2至30个阴性对照和95%至99.9%的置信度提供了适当的因子。我们的新方法比其他非统计过程更可靠,但不需要复杂的计算。只要可以使用阴性对照,它就可以应用于各种免疫测定。

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