Methods for multivariate recurrent event data with measurement error and informative censoring.

ABSTRACT: In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable is observed in a subsample, namely a calibration sample, which can be applied for bias correction. In this article, we develop two non-parametric correction approaches to simultaneously correct for the informative censoring and measurement errors in the analysis of multivariate recurrent event data. A shared frailty model is adopted to characterize the informative censoring and dependence among different types of recurrent events. To adjust for measurement errors, a non-parame

SUBMITTER: Yu H 

PROVIDER: S-EPMC6089684 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature