scholarly journals Multilevel model with random effects for clustered survival data with multiple failure outcomes

2018 ◽  
Vol 38 (6) ◽  
pp. 1036-1055 ◽  
Author(s):  
Richard Tawiah ◽  
Kelvin K.W. Yau ◽  
Geoffrey J. McLachlan ◽  
Suzanne K. Chambers ◽  
Shu-Kay Ng
Keyword(s):  
Random Effects ◽  
Survival Data ◽  
Multilevel Model ◽  
Clustered Survival Data

scholarly journals A NEW MULTILEVEL MODELING APPROACH FOR CLUSTERED SURVIVAL DATA

2020 ◽  
Vol 36 (4) ◽  
pp. 707-750 ◽  
Author(s):  
Jinfeng Xu ◽  
Mu Yue ◽  
Wenyang Zhang
Keyword(s):  
Multilevel Modeling ◽  
Random Effects ◽  
Survival Data ◽  
Birth Interval ◽  
Fixed Effects ◽  
Estimation Procedure ◽  
Unknown Parameters ◽  
Modeling Approach ◽  
Clustered Survival Data ◽  
The Impact

In multilevel modeling of clustered survival data, to account for the differences among different clusters, a commonly used approach is to introduce cluster effects, either random or fixed, into the model. Modeling with random effects may lead to difficulties in the implementation of the estimation procedure for the unknown parameters of interest because the numerical computation of multiple integrals may become unavoidable when the cluster effects are not scalars. On the other hand, if fixed effects are used, there is a danger of having estimators with large variances because there are too many nuisance parameters involved in the model. In this article, using the idea of the homogeneity pursuit, we propose a new multilevel modeling approach for clustered survival data. The proposed modeling approach does not have the potential computational problem as modeling with random effects, and it also involves far fewer unknown parameters than modeling with fixed effects. We also establish asymptotic properties to show the advantages of the proposed model and conduct intensive simulation studies to demonstrate the performance of the proposed method. Finally, the proposed method is applied to analyze a dataset on the second-birth interval in Bangladesh. The most interesting finding is the impact of some important factors on the length of the second-birth interval variation over clusters and its homogeneous structure.

Flexible parametric copula modeling approaches for clustered survival data

2021 ◽  
Author(s):  
Sookhee Kwon ◽  
Il Do Ha ◽  
Jia‐Han Shih ◽  
Takeshi Emura
Keyword(s):  
Survival Data ◽  
Modeling Approaches ◽  
Copula Modeling ◽  
Clustered Survival Data ◽  
Parametric Copula

scholarly journals Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application

2019 ◽  
Vol 19 (4) ◽  
pp. 931-949 ◽  
Author(s):  
Michael J. Crowther
Keyword(s):  
Random Effects ◽  
Survival Data ◽  
Relative Survival ◽  
Survival Models ◽  
Left Truncation ◽  
Delayed Entry ◽  
Working Paper ◽  
Expected Mortality ◽  
Parametric Survival ◽  
Parametric Survival Analysis

In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston–Parmar and the log-hazard equivalent) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods with a commonly used dataset of patients with kidney disease suffering recurrent infections and a simulated example illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statistics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615).

scholarly journals Additive Hazard Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
June Liu ◽  
Yi Zhang
Keyword(s):  
Cohort Studies ◽  
Survival Data ◽  
Failure Time ◽  
Weight Functions ◽  
Disease Rate ◽  
Failure Time Data ◽  
Finite Sample ◽  
Time Data ◽  
Cohort Design ◽  
Clustered Survival Data

The case-cohort design is an effective and economical method in large cohort studies, especially when the disease rate is low. Case-cohort design in most of the existing literature is mainly used to analyze the univariate failure time data. But in practice, multivariate failure time data are commonly encountered in biomedical research. In this paper, we will propose methods based on estimating equation method for case-cohort designs for clustered survival data. By introducing the event failure rate, three different weight functions are constructed. Then, three estimating equations and parameter estimators are presented. Furthermore, consistency and asymptotic normality of the proposed estimators are established. Finally, the simulation results show that the proposed estimation procedure has reasonable finite sample behaviors.

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