A modified two-stage approach for joint modelling of longitudinal and time-to-event data
Joint models for longitudinal and time-to-event data have been applied in many different fields of statistics and clinical studies. However, the main difficulty these models have to face with is the computational problem. The requirement for numerical integration becomes severe when the dimensio...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
TAYLOR & FRANCIS LTD
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/79607 |
| _version_ | 1848764080506011648 |
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| author | Pham, Thi Thu Huong Nur, Darfiana Hoa, Pham Branford, Alan |
| author_facet | Pham, Thi Thu Huong Nur, Darfiana Hoa, Pham Branford, Alan |
| author_sort | Pham, Thi Thu Huong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Joint models for longitudinal and time-to-event data have been
applied in many different fields of statistics and clinical studies.
However, the main difficulty these models have to face with is the
computational problem. The requirement for numerical integration
becomes severe when the dimension of random effects increases.
In this paper, a modified two-stage approach has been proposed
to estimate the parameters in joint models. In particular, in the
first stage, the linear mixed-effects models and best linear unbiased
predictorsare applied to estimate parameters in the longitudinal
submodel. In the second stage, an approximation of the fully joint
log-likelihood is proposed using the estimated the values of these
parameters from the longitudinal submodel. Survival parameters are
estimated bymaximizing the approximation of the fully joint loglikelihood. Simulation studies show that the approach performs well,
especially when the dimension of random effects increases. Finally,
we implement this approach on AIDS data. |
| first_indexed | 2025-11-14T11:13:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-79607 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:13:40Z |
| publishDate | 2018 |
| publisher | TAYLOR & FRANCIS LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-796072020-06-15T00:25:57Z A modified two-stage approach for joint modelling of longitudinal and time-to-event data Pham, Thi Thu Huong Nur, Darfiana Hoa, Pham Branford, Alan Science & Technology Technology Physical Sciences Computer Science, Interdisciplinary Applications Statistics & Probability Computer Science Mathematics Survival data longitudinal data two-stage approach shared random effects approach joint models ERROR SURVIVAL Joint models for longitudinal and time-to-event data have been applied in many different fields of statistics and clinical studies. However, the main difficulty these models have to face with is the computational problem. The requirement for numerical integration becomes severe when the dimension of random effects increases. In this paper, a modified two-stage approach has been proposed to estimate the parameters in joint models. In particular, in the first stage, the linear mixed-effects models and best linear unbiased predictorsare applied to estimate parameters in the longitudinal submodel. In the second stage, an approximation of the fully joint log-likelihood is proposed using the estimated the values of these parameters from the longitudinal submodel. Survival parameters are estimated bymaximizing the approximation of the fully joint loglikelihood. Simulation studies show that the approach performs well, especially when the dimension of random effects increases. Finally, we implement this approach on AIDS data. 2018 Journal Article http://hdl.handle.net/20.500.11937/79607 10.1080/00949655.2018.1518449 English TAYLOR & FRANCIS LTD restricted |
| spellingShingle | Science & Technology Technology Physical Sciences Computer Science, Interdisciplinary Applications Statistics & Probability Computer Science Mathematics Survival data longitudinal data two-stage approach shared random effects approach joint models ERROR SURVIVAL Pham, Thi Thu Huong Nur, Darfiana Hoa, Pham Branford, Alan A modified two-stage approach for joint modelling of longitudinal and time-to-event data |
| title | A modified two-stage approach for joint modelling of longitudinal and time-to-event data |
| title_full | A modified two-stage approach for joint modelling of longitudinal and time-to-event data |
| title_fullStr | A modified two-stage approach for joint modelling of longitudinal and time-to-event data |
| title_full_unstemmed | A modified two-stage approach for joint modelling of longitudinal and time-to-event data |
| title_short | A modified two-stage approach for joint modelling of longitudinal and time-to-event data |
| title_sort | modified two-stage approach for joint modelling of longitudinal and time-to-event data |
| topic | Science & Technology Technology Physical Sciences Computer Science, Interdisciplinary Applications Statistics & Probability Computer Science Mathematics Survival data longitudinal data two-stage approach shared random effects approach joint models ERROR SURVIVAL |
| url | http://hdl.handle.net/20.500.11937/79607 |