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This model is designed to facilitate a detailed exploration of the relationship between various risk factors and the hazard associated with failure time. We develop a sieve maximum likelihood estimation procedure that combines deep neural networks and monotonic splines. By employing deep neural networks, we can effectively capture nonparametric effects, enabling a flexible and adaptive modeling approach for complex relationships. Under certain regularity conditions, we derive a nonasymptotic error bound for the resulting estimator and show that the finite-dimensional estimator is asymptotically normal and achieves the semiparametric efficiency. We conduct simulation studies to evaluate the finite-sample performance of the proposed approach. Furthermore, the proposed method is applied to the Atherosclerosis Risk in Communities study for practical illustration.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag140","type":"journal-article","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T11:40:21Z","timestamp":1784979621000},"source":"Crossref","is-referenced-by-count":0,"title":["Efficient estimation for deep generalized accelerated hazards models with interval-censored data"],"prefix":"10.1093","volume":"82","author":[{"given":"Qiang","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, The Hong Kong Polytechnic University , Hong Kong 999077 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Du","sequence":"additional","affiliation":[{"name":"School of Mathematics, Jilin University , Changchun, Jilin 130012 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