Stringer Alex, Akkaya Hocagil Tugba, Cook Richard J, Ryan Louise M, Jacobson Sandra W, Jacobson Joseph L
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo N2L 3G1, Canada.
Department of Biostatistics, Ankara University, Ankara 06230, Turkey.
Biometrics. 2024 Jul 1;80(3). doi: 10.1093/biomtc/ujae098.
Benchmark dose analysis aims to estimate the level of exposure to a toxin associated with a clinically significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel framework for benchmark dose analysis based on monotone additive dose-response models. We first introduce a flexible approach for fitting monotone additive models via penalized B-splines and Laplace-approximate marginal likelihood. A reflective Newton method is then developed that employs de Boor's algorithm for computing splines and their derivatives for efficient estimation of the benchmark dose. Finally, we develop a novel approach for calculating benchmark dose lower limits based on an approximate pivot for the nonlinear equation solved by the estimated benchmark dose. The favorable properties of this approach compared to the Delta method and a parameteric bootstrap are discussed. We apply the new methods to make inferences about the level of prenatal alcohol exposure associated with clinically significant cognitive defects in children using data from six NIH-funded longitudinal cohort studies. Software to reproduce the results in this paper is available online and makes use of the novel semibmd R package, which implements the methods in this paper.
基准剂量分析旨在估计与具有临床意义的不良结局相关的毒素暴露水平，并使用该水平置信区间的下限来量化不确定性。我们基于单调加性剂量反应模型开发了一种用于基准剂量分析的新框架。我们首先引入一种灵活的方法，通过惩罚B样条和拉普拉斯近似边际似然来拟合单调加性模型。然后开发了一种反射牛顿法，该方法采用德布尔算法来计算样条及其导数，以便有效地估计基准剂量。最后，我们基于估计的基准剂量所求解的非线性方程的近似枢轴，开发了一种计算基准剂量下限的新方法。讨论了该方法与德尔塔法和参数自举法相比的有利特性。我们应用新方法，利用六项由美国国立卫生研究院资助的纵向队列研究的数据，推断与儿童具有临床意义的认知缺陷相关的产前酒精暴露水平。用于重现本文结果的软件可在线获取，并使用了新颖的semibmd R包，该包实现了本文中的方法。
