A Robust Bayesian Random Effects Model for Nonlinear Calibration Problems
A Robust Bayesian Random Effects Model for Nonlinear Calibration Problems
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DOI：
10.1111/j.1541-0420.2012.01762.x
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发表时间：
2012-12-01
期刊：
影响因子：
1.9
通讯作者：
Frahm, N.
中科院分区：
文献类型：
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作者：
Fong, Y.;Wakefield, J.;Frahm, N.
在生物测定或免疫测定的背景下，校准意味着通过在一组含有已知浓度的目标物质的样品上收集的观察结果拟合一条通常是非线性的曲线，然后使用拟合的曲线和在感兴趣的样品上收集的观察结果来预测这些样品中目标物质的浓度。最近的技术进步极大地提高了我们从微量生物样品中定量微量物质的能力。这反过来又导致需要改进校准统计方法。在本文中，我们重点关注开发对相关异常值稳健的校准方法。我们引入了一种新颖的正态混合模型，其具有相关误差项来对实验噪声进行建模。此外，我们提出了五参数逻辑非线性回归模型的重新参数化，使我们能够更好地结合先验信息。我们通过模拟研究检查了我们的方法的性能，并表明它们导致了以估计均方误差和平均预测精度衡量的性能的大幅提高。使用来自 HIV 疫苗试验网络实验室的真实数据示例来说明这些方法。
In the context of a bioassay or an immunoassay, calibration means fitting a curve, usually nonlinear, through the observations collected on a set of samples containing known concentrations of a target substance, and then using the fitted curve and observations collected on samples of interest to predict the concentrations of the target substance in these samples. Recent technological advances have greatly improved our ability to quantify minute amounts of substance from a tiny volume of biological sample. This has in turn led to a need to improve statistical methods for calibration. In this article, we focus on developing calibration methods robust to dependent outliers. We introduce a novel normal mixture model with dependent error terms to model the experimental noise. In addition, we propose a reparameterization of the five parameter logistic nonlinear regression model that allows us to better incorporate prior information. We examine the performance of our methods with simulation studies and show that they lead to a substantial increase in performance measured in terms of mean squared error of estimation and a measure of the average prediction accuracy. A real data example from the HIV Vaccine Trials Network Laboratory is used to illustrate the methods.