{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["8(6)"],"submitter":["Wu P"],"pubmed_abstract":["<h4>Objectives</h4>To address algorithmic bias in clinical prediction models related to the timing of diagnosis, we evaluated the efficacy of mixture cure models that integrate time-to-event and binary classification frameworks to predict diagnoses.<h4>Materials and methods</h4>We conducted a simulation and analyzed real-world North Carolina Medicaid data for children born in 2014, followed until 2023. The study evaluated traditional time-to-event and classification models against mixture cure models under scenarios with varied diagnostic timing and censoring.<h4>Results</h4>Simulation results demonstrated that traditional models exhibit increased bias as diagnosis timing differences widened, whereas mixture cure models yielded unbiased estimates across varying censoring times. In real-wor"],"journal":["JAMIA open"],"pagination":["ooaf148"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12598640"],"repository":["biostudies-literature"],"pubmed_title":["Using mixture cure models to address algorithmic bias in diagnostic timing: autism as a test case."],"pmcid":["PMC12598640"],"pubmed_authors":["Engelhard MM","Goldstein BA","Wu P","Davis NO","Dawson G"],"additional_accession":[]},"is_claimable":false,"name":"Using mixture cure models to address algorithmic bias in diagnostic timing: autism as a test case.","description":"<h4>Objectives</h4>To address algorithmic bias in clinical prediction models related to the timing of diagnosis, we evaluated the efficacy of mixture cure models that integrate time-to-event and binary classification frameworks to predict diagnoses.<h4>Materials and methods</h4>We conducted a simulation and analyzed real-world North Carolina Medicaid data for children born in 2014, followed until 2023. The study evaluated traditional time-to-event and classification models against mixture cure models under scenarios with varied diagnostic timing and censoring.<h4>Results</h4>Simulation results demonstrated that traditional models exhibit increased bias as diagnosis timing differences widened, whereas mixture cure models yielded unbiased estimates across varying censoring times. In real-wor","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-05T13:12:09.437Z","creation":"2026-05-17T03:13:05.416Z"},"accession":"S-EPMC12598640","cross_references":{"pubmed":["41221373"],"doi":["10.1093/jamiaopen/ooaf148"]}}