Variable selection for recurrent event data with informative censoring

Cheng, Ximing; Luo, Li

doi:10.1007/s11424-012-1098-x

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…There have been some developments of regularization approaches with these penalized functions for recurrent event times alone (Tong et al ., 2009; Chen and Wang, 2013; Zhao et al ., 2018). Other related work includes Cheng and Luo (2012) on variable selection for recurrent events data under informative censoring and Wang et al . (2018) on variable selection for panel count data.…”

Section: Introductionmentioning

confidence: 99%

Variable selection in joint frailty models of recurrent and terminal events

Han

Sun

et al. 2020

Biometrics

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Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the "minimum approximated information criterion" method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.

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Section: Introductionmentioning

confidence: 99%

Variable selection in joint frailty models of recurrent and terminal events

Han

Sun

et al. 2020

Biometrics

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“…This approach simultaneously selects variables and estimates regression coefficients. Cheng and Luo (2013) proposed a similar approach and developed a model selection criterion for recurrent event data with informative censoring . Wu (2013) applied a coordinate decent algorithm on the parameter estimation using a LASSO penalized partial likelihood for high‐dimensional recurrent event data .…”

Section: Introductionmentioning

confidence: 99%

Estimate variable importance for recurrent event outcomes with an application to identify hypoglycemia risk factors

Duan

2015

Statistics in Medicine

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Recurrent event data are an important data type for medical research. In particular, many safety endpoints are recurrent outcomes, such as hypoglycemic events. For such a situation, it is important to identify the factors causing these events and rank these factors by their importance. Traditional model selection methods are not able to provide variable importance in this context. Methods that are able to evaluate the variable importance, such as gradient boosting and random forest algorithms, cannot directly be applied to recurrent events data. In this paper, we propose a two-step method that enables us to evaluate the variable importance for recurrent events data. We evaluated the performance of our proposed method by simulations and applied it to a data set from a diabetes study.

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“…This approach simultaneously selects variables and estimates regression coefficients. Cheng and Luo (2013) proposed a similar approach and developed a model selection criterion for recurrent event data with informative censoring [7]. Wu (2013) applied a coordinate decent algorithm on the parameter estimation using a LASSO penalized partial likelihood for high dimensional recurrent event data [8].…”

Section: Introductionmentioning

confidence: 99%

Identifying risk factors associate with hypoglycemic events

Duan

2014

2014 IEEE Symposium on Computational Intelligence in Healthcare and E-Health (CICARE)

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Episodes of hypoglycemia occurred over the study period and is one of the most noticeable adverse events in diabetes care. It is important to identify the factors causing hypoglycemic events and rank these factors by their importance. Most research works only use the time of first hypoglycemia onset and treat it as time to event endpoint due to the limitation of methodology. Traditional model selection methods are not able to provide variable importance in this context. Methods that are able to provide the variable importance, such as gradient boosting and random forest algorithms, cannot directly be applied to recurrent events data. In this paper, we propose a two-step method to identify risk factors that are associate with hypoglycemia. In general, this method allows us to evaluate the variable importance for recurrent events data. The performance of our proposed method are evaluated through intensive simulation studies.

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Variable selection for recurrent event data with informative censoring

Cited by 4 publications

References 18 publications

Variable selection in joint frailty models of recurrent and terminal events

Variable selection in joint frailty models of recurrent and terminal events

Estimate variable importance for recurrent event outcomes with an application to identify hypoglycemia risk factors

Identifying risk factors associate with hypoglycemic events

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