{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T18:52:39Z","timestamp":1781722359527,"version":"3.54.5"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multitype recurrent events frequently arise in lifetime data analysis. It is reasonable to assume that previously occurred events can trigger more events to come, either of homogeneous or of heterogeneous categories. Previous work has focused on nonlinear triggering models for the effects of previous events when there is only a single event type. The standard approach is a proportional intensity model, which captures both the magnitude of the triggering effect and its decay over time via a complicated function of time. We propose a general Cox-type structure for modeling the triggering effects among multiple types of events, with separate triggering effects for each event type on events of interest. The nonlinear model formulation permits parameters to be common across models for different event types. We derive partial likelihood estimators and establish the consistency and asymptotic normality of the estimators, as well as provide plug-in variance estimators. We demonstrate a good performance of the methods through a series of simulation experiments. The models are applied to a cohort of patients with Li-Fraumeni syndrome to analyze the triggering effects between breast cancers and non-breast cancers.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag094","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:22:26Z","timestamp":1781612546000},"source":"Crossref","is-referenced-by-count":0,"title":["Competing-triggering effect models for multitype recurrent event data"],"prefix":"10.1093","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1222-0583","authenticated-orcid":false,"given":"Tianhao","family":"Song","sequence":"first","affiliation":[{"name":"University of North Carolina at Chapel Hill Department of Biostatistics, , Chapel Hill,NC 27599 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Fine","sequence":"additional","affiliation":[{"name":"Pittsburgh Department of Statistics, University of Pittsburgh, , Pennsylvania 15260 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Payal","family":"Khincha","sequence":"additional","affiliation":[{"name":"Clinical Genetics Branch, National Cancer Institute, Rockville , Maryland 20892 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4321-2073","authenticated-orcid":false,"given":"Anastasia","family":"Ivanova","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill Department of Biostatistics, , Chapel Hill,NC 27599 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1658-1068","authenticated-orcid":false,"given":"Paul","family":"Albert","sequence":"additional","affiliation":[{"name":"Biostatistics Branch, National Cancer Institute, Rockville , Maryland 20892 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"issue":",","key":"2026061714091425300_bib1","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1214\/aos\/1176345976","article-title":"Cox\u00e2\u20ac\u2122s regression model for counting processes: A large sample study","volume":"10","author":"Andersen","year":"(1982)","journal-title":"The Annals of Statistics"},{"key":"2026061714091425300_bib2","doi-asserted-by":"publisher","first-page":"126","DOI":"10.5539\/ijsp.v3n3p126","article-title":"Modeling event clustering using the memory Cox-type self-exciting intensity model","volume":"3","author":"Chen","year":"(2014)","journal-title":"International Journal of Statistics and Probability"},{"key":"2026061714091425300_bib3","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"Cox","year":"(1972)","journal-title":"Journal of the Royal Statistical Society, Series B (Methodological)"},{"key":"2026061714091425300_bib4","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1016\/S1470-2045(21)00580-5","article-title":"Cancer incidence, patterns, and genotype\u2013phenotype associations in individuals with pathogenic or likely pathogenic germline TP53 variants: An observational cohort study","volume":"22","author":"de\u00a0Andrade","year":"(2021)","journal-title":"The Lancet, Oncology"},{"key":"2026061714091425300_bib5","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1111\/j.1541-0420.2007.00952.x","article-title":"A joint model for longitudinal measurements and survival data in the presence of multiple failure types","volume":"64","author":"Elashoff","year":"(2008)","journal-title":"Biometrics"},{"key":"2026061714091425300_bib6","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1093\/biomet\/58.1.83","article-title":"Spectra of some self-exciting point processes","volume":"58","author":"Hawkes","year":"1971","journal-title":"Biometrika"},{"key":"2026061714091425300_bib7","doi-asserted-by":"publisher","DOI":"10.1002\/9781118032985","volume-title":"The Statistical Analysis of Failure Time Data","author":"Kalbfleisch","year":"(2002)"},{"key":"2026061714091425300_bib8","doi-asserted-by":"publisher","first-page":"4240","DOI":"10.1002\/sim.8285","article-title":"On the self-triggering Cox model for recurrent event data","volume":"38","author":"Kim","year":"(2019)","journal-title":"Statistics in Medicine"},{"key":"2026061714091425300_bib9","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-94-015-7983-4_14","article-title":"Cox-Type Regression Analysis for Large Numbers of Small Groups of Correlated Failure Time Observations","volume-title":"Survival Analysis: State of the Art. 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