Statistical correction for non-parallelism in a urinary enzyme immunoassay.
Statistical correction for non-parallelism in a urinary enzyme immunoassay.
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尿酶免疫测定中非平行性的统计校正。
DOI：
10.1081/ias-200028078
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Handcock,MarkS
中科院分区：
文献类型：
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作者：
O'Connor,KathleenA;Brindle,Eleanor;Shofer,JaneB;Miller,RebeccaC;Klein,NancyA;Soules,MichaelR;Campbell,KennethL;Mar,Cori;Handcock,MarkS
我们的目的是开发一种统计方法来校正雌酮-3-葡萄糖醛酸苷（E1 G）酶免疫测定（EIA）中的非平行性。在E1 G的EIA中证明了连续稀释尿液标本与校准曲线的不平行性。使用40份尿液标本的线性混合效应分析来模拟E1 G浓度与尿量的关系，并推导出统计学校正。该模型在一个独立的样本上进行了验证，并应用于美国妇女的30个月经周期。测定了专属性、检测限、平行性、回收率、与血清雌二醇的相关性和测定的不精密度。高水平和低水平尿液质控品的试验内和试验间CV均小于14%。整个月经周期的尿E1 G与血清雌二醇高度相关（r= 0.94）。非平行性导致E1 G浓度随尿量增加而降低（斜率=-0.210，p < 0.0001）。在50%抑制下，该试验与E1 G的交叉反应性为100%，与17β-雌二醇3-葡萄糖醛酸苷的交叉反应性为83%。后者的剂量-反应曲线与E1 G的剂量-反应曲线不平行，这可能是不平行的原因。将E1 G浓度调整为标准化尿量的统计学校正在24份独立标本中产生了平行性（斜率= −0.043 ± 0.010），并将各稀释度E1 G浓度的平均CV从校正前的19.5% ± 5.6%改善至校正后的10.3% ± 5.3%。基于线性混合效应模型的统计方法是校正非平行性的一种方便方法，特别是对于将进行汇总分析的激素数据。
Our aim was to develop a statistical method to correct for non‐parallelism in an estrone‐3‐glucuronide (E1G) enzyme immunoassay (EIA). Non‐parallelism of serially diluted urine specimens with a calibration curve was demonstrated in an EIA for E1G. A linear mixed‐effects analysis of 40 urine specimens was used to model the relationship of E1G concentration with urine volume and derive a statistical correction. The model was validated on an independent sample and applied to 30 menstrual cycles from American women. Specificity, detection limit, paral‐lelism, recovery, correlation with serum estradiol, and imprecision of the assay were determined. Intra‐and inter‐assay CVs were less than 14% for high‐ and low‐urine controls. Urinary E1G across the menstrual cycle was highly correlated with serum estradiol (r= 0.94). Non‐parallelism produced decreasing E1G concentration with increase in urine volume (slope = −0.210,p< 0.0001). At 50% inhibition, the assay had 100% cross‐reactivity with E1G and 83% with 17β‐estradiol 3‐glucuronide. The dose–response curve of the latter did not parallel that of E1G and is a possible cause of the non‐parallelism. The statistical correction adjusting E1G concentration to a standardized urine volume produced parallelism in 24 independent specimens (slope = −0.043 ± 0.010), and improved the average CV of E1G concentration across dilutions from 19.5% ± 5.6% before correction to 10.3% ± 5.3% after correction. A statistical method based on linear mixed effects modeling is an expedient approach for correction of non‐parallelism, particularly for hormone data that will be analyzed in aggregate.