Analysis of multivariate recurrent event data with time-dependent covariates and informative censoring

2012 ◽  
Vol 54 (5) ◽  
pp. 585-599 ◽  
Author(s):  
Xingqiu Zhao ◽  
Li Liu ◽  
Yanyan Liu ◽  
Wei Xu
Keyword(s):  
Informative Censoring ◽  
Recurrent Event ◽  
Time Dependent ◽  
Event Data ◽  
Recurrent Event Data ◽  
Time Dependent Covariates

Additive rates model for recurrent event data with intermittently observed time-dependent covariates

2021 ◽  
Vol 30 (10) ◽  
pp. 2239-2255
Author(s):  
Tianmeng Lyu ◽  
Xianghua Luo ◽  
Chiung-Yu Huang ◽  
Yifei Sun
Keyword(s):  
Recurrent Events ◽  
Epidemiologic Study ◽  
Recurrent Event ◽  
Time Dependent ◽  
Occurrence Rate ◽  
Absolute Difference ◽  
Event Data ◽  
Recurrent Event Data ◽  
Time Dependent Covariates

Various regression methods have been proposed for analyzing recurrent event data. Among them, the semiparametric additive rates model is particularly appealing because the regression coefficients quantify the absolute difference in the occurrence rate of the recurrent events between different groups. Estimation of the additive rates model requires the values of time-dependent covariates being observed throughout the entire follow-up period. In practice, however, the time-dependent covariates are usually only measured at intermittent follow-up visits. In this paper, we propose to kernel smooth functions involving time-dependent covariates across subjects in the estimating function, as opposed to imputing individual covariate trajectories. Simulation studies show that the proposed method outperforms simple imputation methods. The proposed method is illustrated with data from an epidemiologic study of the effect of streptococcal infections on recurrent pharyngitis episodes.

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

Biometrics ◽  
2018 ◽  
Vol 74 (3) ◽  
pp. 966-976 ◽  
Author(s):  
Hsiang Yu ◽  
Yu-Jen Cheng ◽  
Ching-Yun Wang
Keyword(s):  
Measurement Error ◽  
Informative Censoring ◽  
Recurrent Event ◽  
Event Data ◽  
Recurrent Event Data

scholarly journals Analyzing Recurrent Event Data With Informative Censoring

2001 ◽  
Vol 96 (455) ◽  
pp. 1057-1065 ◽  
Author(s):  
Mei-Cheng Wang ◽  
Jing Qin ◽  
Chin-Tsang Chiang
Keyword(s):  
Informative Censoring ◽  
Recurrent Event ◽  
Event Data ◽  
Recurrent Event Data

scholarly journals Regression splines in the time-dependent coefficient rates model for recurrent event data

2008 ◽  
Vol 27 (28) ◽  
pp. 5890-5906 ◽  
Author(s):  
Leila D. Amorim ◽  
Jianwen Cai ◽  
Donglin Zeng ◽  
Maurício L. Barreto
Keyword(s):  
Recurrent Event ◽  
Time Dependent ◽  
Regression Splines ◽  
Event Data ◽  
Recurrent Event Data

scholarly journals Control-based imputation for sensitivity analyses in informative censoring for recurrent event data

2017 ◽  
Vol 16 (6) ◽  
pp. 424-432 ◽  
Author(s):  
Fei Gao ◽  
Guanghan F. Liu ◽  
Donglin Zeng ◽  
Lei Xu ◽  
Bridget Lin ◽  
...  
Keyword(s):  
Informative Censoring ◽  
Recurrent Event ◽  
Sensitivity Analyses ◽  
Event Data ◽  
Recurrent Event Data

scholarly journals Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Hsiang Yu ◽  
Yu-Jen Cheng ◽  
Ching-Yun Wang
Keyword(s):  
Measurement Error ◽  
Recurrent Events ◽  
Semiparametric Regression ◽  
Informative Censoring ◽  
Recurrent Event ◽  
Occurrence Rate ◽  
Event Data ◽  
Recurrent Event Data ◽  
Finite Sample ◽  
Cancer Trial

AbstractRecurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections of HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out. Further, some covariates may be measured with errors. To accommodate for both informative censoring and measurement error, the occurrence of recurrent events is modelled through an unspecified frailty distribution and accompanied with a classical measurement error model. We propose two corrected approaches based on different ideas, and we show that they are numerically identical when estimating the regression parameters. The asymptotic properties of the proposed estimators are established, and the finite sample performance is examined via simulations. The proposed methods are applied to the Nutritional Prevention of Cancer trial for assessing the effect of the plasma selenium treatment on the recurrence of squamous cell carcinoma.

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