Semiparametric analysis for recurrent event data with time-dependent covariates and informative censoring.
TLDR
This article proposes a novel semiparametric inference procedure that depends on neither the frailty nor the censoring time distribution, and incorporates both time-dependent and time-independent covariates in the formulation.Abstract:
Recurrent event data analyses are usually conducted under the assumption that the censoring time is independent of the recurrent event process. In many applications the censoring time can be informative about the underlying recurrent event process, especially in situations where a correlated failure event could potentially terminate the observation of recurrent events. In this article, we consider a semiparametric model of recurrent event data that allows correlations between censoring times and recurrent event process via frailty. This flexible framework incorporates both time-dependent and time-independent covariates in the formulation, while leaving the distributions of frailty and censoring times unspecified. We propose a novel semiparametric inference procedure that depends on neither the frailty nor the censoring time distribution. Large sample properties of the regression parameter estimates and the estimated baseline cumulative intensity functions are studied. Numerical studies demonstrate that the proposed methodology performs well for realistic sample sizes. An analysis of hospitalization data for patients in an AIDS cohort study is presented to illustrate the proposed method.read more
Citations
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Journal ArticleDOI
Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events.
TL;DR: A three-level semiparametric regression model is proposed for jointly modeling the time to a failure event, the backward recurrentevent process, and the marker observed at the time of each backward recurrent event.
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Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study.
Jing Ning,Mohammad H. Rahbar,Mohammad H. Rahbar,Sangbum Choi,Jin Piao,Chuan Hong,Deborah J. del Junco,Elaheh Rahbar,Erin E. Fox,John B. Holcomb,Mei Cheng Wang +10 more
TL;DR: This work uses semiparametric rate models for multivariate recurrent events to estimate blood product ratios and uses latent variables to account for multiple sources of informative censoring, and applies the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study.
Journal ArticleDOI
A nonparametric regression model for panel count data analysis
TL;DR: In this article, a non-parametric regression model for panel count data is proposed to analyze the non-linear effect of one of interleukin functions with the risk of childhood wheezing.
Journal ArticleDOI
Semiparametric Regression Analysis of Panel Count Data: A Practical Review.
TL;DR: Focusing on a practical setting where the effects of some time-independent covariates on the recurrent events are of primary interest, semiparametric regression modelling approaches for panel count data that have been implemented in R package spef are reviewed.
Journal ArticleDOI
A Marginal Additive Rates Model for Recurrent Event Data with a Terminal Event
Hui Zhao,Jie Zhou,Liuquan Sun +2 more
TL;DR: In this paper, a marginal additive rates model for recurrent events with a terminal event was proposed, and two procedures for estimating the model parameters were developed to check the asymptotic properties of the resulting estimators.
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