Survival mixture modelling of recurrent infections
Recurrent infections data are commonly encountered in biomedical applications, where the recurrent events are characterised by an acute phase followed by a stable phase after the index episode. Two-component survival mixture models, in both proportional hazards and accelerated failure time settings,...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Japanese Society of Computational Statistics
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/46232 |
| _version_ | 1848757502207852544 |
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| author | Lee, Andy Zhao, Yun Yau, Kelvin Ng, Shu |
| author2 | Masahiro Mizuta |
| author_facet | Masahiro Mizuta Lee, Andy Zhao, Yun Yau, Kelvin Ng, Shu |
| author_sort | Lee, Andy |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Recurrent infections data are commonly encountered in biomedical applications, where the recurrent events are characterised by an acute phase followed by a stable phase after the index episode. Two-component survival mixture models, in both proportional hazards and accelerated failure time settings, are presented as a flexible method of analysing such data. To account for the inherent dependency of the recurrent observations, random effects are incorporated within the conditional hazard function. Assuming a Weibull or log-logistic baseline hazard in both mixture components of the survival mixture model, an EM algorithm is developed for the residual maximum quasi-likelihood estimation of fixed effect and variance components parameters. The methodology is implemented as a graphical user interface coded using Microsoft visual C++. Application to model recurrent urinary tract infections for elderly women is illustrated, where significant individual variations are evident at both acute and stable phases. The survival mixture methodology developed enable practitioners to identify pertinent risk factors affecting the recurrent times and to draw valid conclusions inferred from these correlated and heterogeneous survival data. |
| first_indexed | 2025-11-14T09:29:07Z |
| format | Conference Paper |
| id | curtin-20.500.11937-46232 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:29:07Z |
| publishDate | 2008 |
| publisher | Japanese Society of Computational Statistics |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-462322022-12-07T06:50:50Z Survival mixture modelling of recurrent infections Lee, Andy Zhao, Yun Yau, Kelvin Ng, Shu Masahiro Mizuta Junji Nakano Recurrent infections data are commonly encountered in biomedical applications, where the recurrent events are characterised by an acute phase followed by a stable phase after the index episode. Two-component survival mixture models, in both proportional hazards and accelerated failure time settings, are presented as a flexible method of analysing such data. To account for the inherent dependency of the recurrent observations, random effects are incorporated within the conditional hazard function. Assuming a Weibull or log-logistic baseline hazard in both mixture components of the survival mixture model, an EM algorithm is developed for the residual maximum quasi-likelihood estimation of fixed effect and variance components parameters. The methodology is implemented as a graphical user interface coded using Microsoft visual C++. Application to model recurrent urinary tract infections for elderly women is illustrated, where significant individual variations are evident at both acute and stable phases. The survival mixture methodology developed enable practitioners to identify pertinent risk factors affecting the recurrent times and to draw valid conclusions inferred from these correlated and heterogeneous survival data. 2008 Conference Paper http://hdl.handle.net/20.500.11937/46232 Japanese Society of Computational Statistics fulltext |
| spellingShingle | Lee, Andy Zhao, Yun Yau, Kelvin Ng, Shu Survival mixture modelling of recurrent infections |
| title | Survival mixture modelling of recurrent infections |
| title_full | Survival mixture modelling of recurrent infections |
| title_fullStr | Survival mixture modelling of recurrent infections |
| title_full_unstemmed | Survival mixture modelling of recurrent infections |
| title_short | Survival mixture modelling of recurrent infections |
| title_sort | survival mixture modelling of recurrent infections |
| url | http://hdl.handle.net/20.500.11937/46232 |