Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis
In modeling multivariate failure time data, a class of survival model with random effects is applicable. It incorporates the random effect terms in the linear predictor and includes various random effect survival models as special cases, such as the random effect model assuming Cox's proportion...
| Main Authors: | , , , |
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| Format: | Journal Article |
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Elsevier Science
2007
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| Online Access: | http://hdl.handle.net/20.500.11937/8936 |
| _version_ | 1848745804736495616 |
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| author | Xiang, L. Yau, K. Tse, S. Lee, Andy |
| author_facet | Xiang, L. Yau, K. Tse, S. Lee, Andy |
| author_sort | Xiang, L. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In modeling multivariate failure time data, a class of survival model with random effects is applicable. It incorporates the random effect terms in the linear predictor and includes various random effect survival models as special cases, such as the random effect model assuming Cox's proportional hazards, with Weibull baseline hazards and with power family of transformation in the relative risk function. Residual maximum likelihood (REML) estimation of parameters is achieved by adopting the generalised linear mixed models (GLMM) approach. Accordingly, influence diagnostics are developed as sensitivity measures for the REML estimation of model parameters. A data set of recurrent infections of kidney patients on portable dialysis illustrates the usefulness of the influence diagnostics. A simulation study is carried out to examine the performance of the proposed influence diagnostics. |
| first_indexed | 2025-11-14T06:23:11Z |
| format | Journal Article |
| id | curtin-20.500.11937-8936 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:23:11Z |
| publishDate | 2007 |
| publisher | Elsevier Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-89362017-09-13T14:37:52Z Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis Xiang, L. Yau, K. Tse, S. Lee, Andy Influence diagnostics Local influence Generalised linear mixed models Random effects Multivariate failure times Weibull distribution In modeling multivariate failure time data, a class of survival model with random effects is applicable. It incorporates the random effect terms in the linear predictor and includes various random effect survival models as special cases, such as the random effect model assuming Cox's proportional hazards, with Weibull baseline hazards and with power family of transformation in the relative risk function. Residual maximum likelihood (REML) estimation of parameters is achieved by adopting the generalised linear mixed models (GLMM) approach. Accordingly, influence diagnostics are developed as sensitivity measures for the REML estimation of model parameters. A data set of recurrent infections of kidney patients on portable dialysis illustrates the usefulness of the influence diagnostics. A simulation study is carried out to examine the performance of the proposed influence diagnostics. 2007 Journal Article http://hdl.handle.net/20.500.11937/8936 10.1016/j.csda.2006.11.023 Elsevier Science restricted |
| spellingShingle | Influence diagnostics Local influence Generalised linear mixed models Random effects Multivariate failure times Weibull distribution Xiang, L. Yau, K. Tse, S. Lee, Andy Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis |
| title | Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis |
| title_full | Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis |
| title_fullStr | Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis |
| title_full_unstemmed | Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis |
| title_short | Influence diagnostics for random effect survival models: Application to a recurrent infection study for kidney patients on portable dialysis |
| title_sort | influence diagnostics for random effect survival models: application to a recurrent infection study for kidney patients on portable dialysis |
| topic | Influence diagnostics Local influence Generalised linear mixed models Random effects Multivariate failure times Weibull distribution |
| url | http://hdl.handle.net/20.500.11937/8936 |