{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Stringer A"],"funding":["Natural Sciences and Engineering Research Council of Canada","National Institute on Alcohol Abuse and Alcoholism","Institute on Child Health and Human Development","NIDA NIH HHS","NIAAA NIH HHS","NIDA","National Institute on Drug Abuse","Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers","Canadian Institutes of Health Research","CIHR"],"pagination":["ujae098"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11403299"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["80(3)"],"pubmed_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 estima"],"journal":["Biometrics"],"pubmed_title":["Semi-parametric benchmark dose analysis with monotone additive models."],"pmcid":["PMC11403299"],"funding_grant_id":["R01 DA008916","R01 DA17786","P50 AA07606","R01 AA009524","R01 AA09524","R01 DA05460","FRN PJT-180551","R01 DA00090","R01 AA18116","R01 AA13272","HD036890","R01 AA14215","R01 DA017786","R01 AA018116","R29 DA005460","R01 DA03874","R21 DA021034","R01 AA06666","R01 AA006666","R01 AA08105","R01 AA10108","R01 AA06966","R01 DA12401","RGPIN-03331-2023","CE140100049","R01 AA025905","R21 AA014215","R01 DA012401","R01 AA06390","R01 DA0737362","R01-DA08916","R01 DA03209","R01 AA001455","RGPIN2017-04207","R01 DA06839","R01 AA006966"],"pubmed_authors":["Akkaya Hocagil T","Jacobson SW","Cook RJ","Jacobson JL","Stringer A","Ryan LM"],"additional_accession":[]},"is_claimable":false,"name":"Semi-parametric benchmark dose analysis with monotone additive models.","description":"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 estima","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-05-03T03:21:54.322Z","creation":"2025-04-20T00:42:55.835Z"},"accession":"S-EPMC11403299","cross_references":{"pubmed":["39282733"],"doi":["10.1093/biomtc/ujae098"]}}