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ELISA 条件下浓度的区间估计。

Interval estimation for concentration in the ELISA setting.

机构信息

Department of Mathematics & Statistics, University of New Brunswick - Saint John, 100 Tucker Park Road, P.O. Box 5050, Saint John, New Brunswick E2L 4L5, Canada; Quantics Biostatistics, Exchange Tower, 19 Canning Street, Edinburgh EH3 8EG, United Kingdom.

Quantics Biostatistics, Exchange Tower, 19 Canning Street, Edinburgh EH3 8EG, United Kingdom.

出版信息

J Immunol Methods. 2021 Oct;497:113122. doi: 10.1016/j.jim.2021.113122. Epub 2021 Aug 6.

Abstract

Enzyme-linked immunosorbent assays (ELISAs) are often used to quantify the concentration of biological substances. In a typical analysis only a point estimate of the concentration will be presented as interval estimation continues to present challenges for non-linear dose-response models. In this setting, interval estimates calculated using a Wald approach can suffer from poor coverage and have limits that fall outside parameter boundaries. Here we compare profile likelihood interval estimation procedures to Wald type intervals for the interval estimation of a concentration in the ELISA setting. Through a comprehensive simulation study, it is shown that profile likelihood methods result in interval estimates with superior coverage and that are more robust to differences in assay design when compared to Wald based approaches.

摘要

酶联免疫吸附测定(ELISA)常用于定量生物物质的浓度。在典型的分析中,仅会呈现浓度的点估计值,因为对于非线性剂量反应模型,区间估计仍然存在挑战。在这种情况下,使用 Wald 方法计算的区间估计可能会出现覆盖范围不佳的情况,并且其界限会落在参数边界之外。在这里,我们将比较似然比区间估计程序与 Wald 型区间在 ELISA 环境中浓度的区间估计。通过全面的模拟研究表明,似然比方法产生的区间估计具有更好的覆盖范围,并且与基于 Wald 的方法相比,对于检测设计的差异更稳健。

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