Semi-parametric benchmark dose analysis with monotone additive models.

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-07-01 DOI:10.1093/biomtc/ujae098
Alex Stringer, Tugba Akkaya Hocagil, Richard J Cook, Louise M Ryan, Sandra W Jacobson, Joseph L Jacobson
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引用次数: 0

Abstract

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.

使用单调相加模型进行半参数基准剂量分析
基准剂量分析旨在估算与临床显著不良结果相关的毒素暴露水平,并利用该水平置信区间的下限来量化不确定性。我们基于单调相加剂量反应模型开发了一种新的基准剂量分析框架。我们首先介绍了一种灵活的方法,通过受惩罚的 B-样条曲线和拉普拉斯近似边际似然法拟合单调相加模型。然后,我们开发了一种反射牛顿方法,该方法采用 de Boor 算法计算样条及其导数,从而高效地估算基准剂量。最后,我们根据估计基准剂量所求解的非线性方程的近似支点,开发了一种计算基准剂量下限的新方法。我们讨论了这种方法与德尔塔法和参数自举法相比的有利特性。我们利用美国国立卫生研究院(NIH)资助的六项纵向队列研究数据,运用新方法推断了与临床上重大儿童认知缺陷相关的产前酒精暴露水平。重现本文结果的软件可在线获取,该软件使用了新颖的 semibmd R 软件包,该软件包实现了本文的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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