biostudies-literatureStringer ANatural Sciences and Engineering Research Council of CanadaNational Institute on Alcohol Abuse and AlcoholismInstitute on Child Health and Human DevelopmentNIDA NIH HHSNIAAA NIH HHSNIDANational Institute on Drug AbuseAustralian Research Council Centre of Excellence for Mathematical and Statistical FrontiersCanadian Institutes of Health ResearchCIHRujae098https://www.ebi.ac.uk/biostudies/studies/S-EPMC11403299biostudies-literatureUnknown80(3)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 estimaBiometricsSemi-parametric benchmark dose analysis with monotone additive models.PMC11403299R01 DA008916R01 DA17786P50 AA07606R01 AA009524R01 AA09524R01 DA05460FRN PJT-180551R01 DA00090R01 AA18116R01 AA13272HD036890R01 AA14215R01 DA017786R01 AA018116R29 DA005460R01 DA03874R21 DA021034R01 AA06666R01 AA006666R01 AA08105R01 AA10108R01 AA06966R01 DA12401RGPIN-03331-2023CE140100049R01 AA025905R21 AA014215R01 DA012401R01 AA06390R01 DA0737362R01-DA08916R01 DA03209R01 AA001455RGPIN2017-04207R01 DA06839R01 AA006966Akkaya Hocagil TJacobson SWCook RJJacobson JLStringer ARyan LMfalseSemi-parametric benchmark dose analysis with monotone additive models.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 estima2024-01-01T00:00:00Z2024 Jul2026-05-03T03:21:54.322Z2025-04-20T00:42:55.835ZS-EPMC114032993928273310.1093/biomtc/ujae098