Semi-parametric benchmark dose analysis with monotone additive models

Stringer A., AKKAYA HOCAGİL T., Cook R. J., Ryan L. M., Jacobson S. W., Jacobson J. L.

Biometrics, cilt.80, sa.3, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 80 Sayı: 3
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1093/biomtc/ujae098
  • Dergi Adı: Biometrics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Aqualine, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Computer & Applied Sciences, EMBASE, Geobase, SportDiscus, Veterinary Science Database, zbMATH
  • Anahtar Kelimeler: additive model, benchmark dose analysis, Laplace approximation, marginal likelihood, monotone smoothing
  • Ankara Üniversitesi Adresli: Evet

Özet

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.