Article ; Online: Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis.
Multivariate behavioral research
2023 Volume 59, Issue 1, Page(s) 171–186
Abstract: A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose ...
Abstract | A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters. Simulation factors included the value of the between-clusters variance, number of clusters, within-cluster sample size, Weibull scale parameter, and mobility rate. The results suggest that substantial relative parameter bias, unacceptable coverage of the 95% confidence intervals, and severely biased standard errors are possible for all model parameters when a discrete-time survival model is used that ignores the cross-classified data structure. The findings presented in this study are useful for methodologists and practitioners in educational research, public health, and other social sciences where discrete-time survival analysis is a common methodological technique for analyzing event-history data. |
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MeSH term(s) | Humans ; Models, Statistical ; Computer Simulation ; Survival Analysis ; Monte Carlo Method ; Multilevel Analysis |
Language | English |
Publishing date | 2023-09-04 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 1532-7906 |
ISSN (online) | 1532-7906 |
DOI | 10.1080/00273171.2023.2230481 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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