Recurrent Events Analysis in the Presence of Terminal Event and Zero-recurrence Subjects

Zhao, Xiaobing; Wang, Jinglong; Zhou, Xian; Zhu, Zhongyi

doi:10.1080/03610926.2012.753085

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Because the elderly subjects in the trial are at least 70 years of age, a likely reason for the positive age effect is that an older age beyond 70 could start preventing patients from potential triggers for serious fall, such as exercise or intensive movement. This finding is consistent with previous analysis of the trial where mortality is considered as a competing risk (Chen et al., 2021; Chen & Li, 2022; Zhao et al., 2022). Finally, white female patients with a higher number of chronic conditions are more prone to shorter average times to the first serious fall‐related injury, under either treatment condition.…”

Section: Applications To Analyses Of Clustered Time‐to‐event Datasupporting

confidence: 92%

Clustered restricted mean survival time regression

Chen

Harhay

2022

Biometrical J

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For multicenter randomized trials or multilevel observational studies, the Cox regression model has long been the primary approach to study the effects of covariates on time‐to‐event outcomes. A critical assumption of the Cox model is the proportionality of the hazard functions for modeled covariates, violations of which can result in ambiguous interpretations of the hazard ratio estimates. To address this issue, the restricted mean survival time (RMST), defined as the mean survival time up to a fixed time in a target population, has been recommended as a model‐free target parameter. In this article, we generalize the RMST regression model to clustered data by directly modeling the RMST as a continuous function of restriction times with covariates while properly accounting for within‐cluster correlations to achieve valid inference. The proposed method estimates regression coefficients via weighted generalized estimating equations, coupled with a cluster‐robust sandwich variance estimator to achieve asymptotically valid inference with a sufficient number of clusters. In small‐sample scenarios where a limited number of clusters are available, however, the proposed sandwich variance estimator can exhibit negative bias in capturing the variability of regression coefficient estimates. To overcome this limitation, we further propose and examine bias‐corrected sandwich variance estimators to reduce the negative bias of the cluster‐robust sandwich variance estimator. We study the finite‐sample operating characteristics of proposed methods through simulations and reanalyze two multicenter randomized trials.

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Section: Applications To Analyses Of Clustered Time‐to‐event Datasupporting

confidence: 92%

Clustered restricted mean survival time regression

Chen

Harhay

2022

Biometrical J

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“…Many studies have been done on cure models [14][15][16][17]. In the context of recurrent event data, Rondeau et al, proposed a frailty model for the recurrent events in the presence of cure fraction [13].…”

Section: Introductionmentioning

confidence: 99%

“…In the context of recurrent event data, Rondeau et al, proposed a frailty model for the recurrent events in the presence of cure fraction [13]. Zhao et al, introduced a new model for recurrent with terminal events which can incorporated zero recurrence subjects [17]. Kim proposed a joint model for recurrent with a terminal event in the presence of cure fraction.…”

Section: Introductionmentioning

confidence: 99%

Joint frailty model for recurrent events and a terminal event in the presence of cure fraction

Borzu

Baghestani

Talebi-Ghane

et al. 2023

ebph

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The observations of repeated or recurrent events occur in many longitudinal studies. Furthermore, sometimes there may exist a terminal event such as death, which is strongly correlated with recurrent events. In many situations, a fraction of subjects who will never experience the event of interest during a long follow-up period is considered to be cured. In this article, we proposed a joint frailty model in the presence of cure fraction. The dependency is modeled by shared frailty that is contained in both the recurrent andterminal events hazard functions. It allows to estimate two separate sets of parameters on the recurrent, death, and cure model. We applied the maximum likelihood method under a piecewise constant hazard function for model fitting. The proposed model is evaluated by simulation studies and an application to a breast cancer data is provided.

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Robust variable selection of joint frailty model for panel count data

Wang

Zhao

et al. 2018

Journal of Multivariate Analysis

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Recurrent Events Analysis in the Presence of Terminal Event and Zero-recurrence Subjects

Cited by 5 publications

References 20 publications

Clustered restricted mean survival time regression

Clustered restricted mean survival time regression

Joint frailty model for recurrent events and a terminal event in the presence of cure fraction

Robust variable selection of joint frailty model for panel count data

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