Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
Marginal proportional hazards models for multivariate interval-censored data
Biometrika. 2023 Sep;110(3):815-830. doi: 10.1093/biomet/asac059. Epub 2022 Nov 2.ABSTRACTMultivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption ...
Source: Biometrika - August 21, 2023 Category: Biotechnology Authors: Yangjianchen Xu Donglin Zeng D Y Lin Source Type: research
