{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhan T"],"funding":["NIDDK NIH HHS"],"pagination":["222-237"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12893071"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["48(2)"],"pubmed_abstract":["Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease. A general area of application involves a recurrent event with non-negligible duration (e.g., hospitalization and associated length of hospital stay) and subject to a terminating event (e.g., death). We propose a semiparametric multiplicative model for the process version of the probability of being alive and in the (transient) state of interest. Under the proposed methods, the regression paramet"],"journal":["The Canadian journal of statistics = Revue canadienne de statistique"],"pubmed_title":["Semiparametric regression methods for temporal processes subject to multiple sources of censoring."],"pmcid":["PMC12893071"],"funding_grant_id":["R01 DK070869"],"pubmed_authors":["Zhan T","Schaubel DE"],"additional_accession":[]},"is_claimable":false,"name":"Semiparametric regression methods for temporal processes subject to multiple sources of censoring.","description":"Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease. A general area of application involves a recurrent event with non-negligible duration (e.g., hospitalization and associated length of hospital stay) and subject to a terminating event (e.g., death). We propose a semiparametric multiplicative model for the process version of the probability of being alive and in the (transient) state of interest. Under the proposed methods, the regression paramet","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Jun","modification":"2026-07-15T18:01:20.902Z","creation":"2026-07-08T03:08:24.537Z"},"accession":"S-EPMC12893071","cross_references":{"pubmed":["41684372"],"doi":["10.1002/cjs.11528"]}}