Penalized spline joint models for longitudinal and time-to-event data
The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses ofte...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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TAYLOR & FRANCIS INC
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/79610 |
| _version_ | 1848764081389961216 |
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| author | Huong, P.T.T. Nur, Darfiana Branford, A. |
| author_facet | Huong, P.T.T. Nur, Darfiana Branford, A. |
| author_sort | Huong, P.T.T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models. |
| first_indexed | 2025-11-14T11:13:41Z |
| format | Journal Article |
| id | curtin-20.500.11937-79610 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:13:41Z |
| publishDate | 2017 |
| publisher | TAYLOR & FRANCIS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-796102020-06-15T00:28:27Z Penalized spline joint models for longitudinal and time-to-event data Huong, P.T.T. Nur, Darfiana Branford, A. Science & Technology Physical Sciences Statistics & Probability Mathematics Joint models longitudinal data random effects survival data time-dependent covariates SURVIVAL The joint models for longitudinal data and time-to-event data have recently received numerous attention in clinical and epidemiologic studies. Our interest is in modeling the relationship between event time outcomes and internal time-dependent covariates. In practice, the longitudinal responses often show non linear and fluctuated curves. Therefore, the main aim of this paper is to use penalized splines with a truncated polynomial basis to parameterize the non linear longitudinal process. Then, the linear mixed-effects model is applied to subject-specific curves and to control the smoothing. The association between the dropout process and longitudinal outcomes is modeled through a proportional hazard model. Two types of baseline risk functions are considered, namely a Gompertz distribution and a piecewise constant model. The resulting models are referred to as penalized spline joint models; an extension of the standard joint models. The expectation conditional maximization (ECM) algorithm is applied to estimate the parameters in the proposed models. To validate the proposed algorithm, extensive simulation studies were implemented followed by a case study. In summary, the penalized spline joint models provide a new approach for joint models that have improved the existing standard joint models. 2017 Journal Article http://hdl.handle.net/20.500.11937/79610 10.1080/03610926.2016.1235195 English TAYLOR & FRANCIS INC restricted |
| spellingShingle | Science & Technology Physical Sciences Statistics & Probability Mathematics Joint models longitudinal data random effects survival data time-dependent covariates SURVIVAL Huong, P.T.T. Nur, Darfiana Branford, A. Penalized spline joint models for longitudinal and time-to-event data |
| title | Penalized spline joint models for longitudinal and time-to-event data |
| title_full | Penalized spline joint models for longitudinal and time-to-event data |
| title_fullStr | Penalized spline joint models for longitudinal and time-to-event data |
| title_full_unstemmed | Penalized spline joint models for longitudinal and time-to-event data |
| title_short | Penalized spline joint models for longitudinal and time-to-event data |
| title_sort | penalized spline joint models for longitudinal and time-to-event data |
| topic | Science & Technology Physical Sciences Statistics & Probability Mathematics Joint models longitudinal data random effects survival data time-dependent covariates SURVIVAL |
| url | http://hdl.handle.net/20.500.11937/79610 |