<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Lyu T</submitter><funding>NCATS NIH HHS</funding><funding>NIMH NIH HHS</funding><funding>NCI NIH HHS</funding><pagination>615-633</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9232183</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>19(4)</volume><pubmed_abstract>Regression methods, including the proportional rates model and additive rates model, have been proposed to evaluate the effect of covariates on the risk of recurrent events. These two models have different assumptions on the form of the covariate effects. A more flexible model, the additive-multiplicative rates model, is considered to allow the covariates to have both additive and multiplicative effects on the marginal rate of recurrent event process. However, its use is limited to the cases where the time-dependent covariates are monitored continuously throughout the follow-up time. In practice, time-dependent covariates are often only measured intermittently, which renders the current estimation method for the additive-multiplicative rates model inapplicable. In this paper, we propose a </pubmed_abstract><journal>Journal of data science : JDS</journal><pubmed_title>Additive-Multiplicative Rates Model for Recurrent Event Data with Intermittently Observed Time-Dependent Covariates.</pubmed_title><pmcid>PMC9232183</pmcid><funding_grant_id>R03 MH112895</funding_grant_id><funding_grant_id>P30 CA077598</funding_grant_id><funding_grant_id>UL1 TR000114</funding_grant_id><pubmed_authors>Lyu T</pubmed_authors><pubmed_authors>Luo X</pubmed_authors><pubmed_authors>Sun Y</pubmed_authors></additional><is_claimable>false</is_claimable><name>Additive-Multiplicative Rates Model for Recurrent Event Data with Intermittently Observed Time-Dependent Covariates.</name><description>Regression methods, including the proportional rates model and additive rates model, have been proposed to evaluate the effect of covariates on the risk of recurrent events. These two models have different assumptions on the form of the covariate effects. A more flexible model, the additive-multiplicative rates model, is considered to allow the covariates to have both additive and multiplicative effects on the marginal rate of recurrent event process. However, its use is limited to the cases where the time-dependent covariates are monitored continuously throughout the follow-up time. In practice, time-dependent covariates are often only measured intermittently, which renders the current estimation method for the additive-multiplicative rates model inapplicable. In this paper, we propose a </description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Oct</publication><modification>2025-04-26T20:44:03.67Z</modification><creation>2025-04-06T16:34:53.545Z</creation></dates><accession>S-EPMC9232183</accession><cross_references><pubmed>35757199</pubmed><doi>10.6339/21-jds1027</doi></cross_references></HashMap>