A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis
Bayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on estimating parameters in a proposed fully Bayesian analysis setting for the penalized...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2020
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| Online Access: | http://hdl.handle.net/20.500.11937/88546 |
| _version_ | 1848765039850291200 |
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| author | Thi Thu Pham, H. Pham, H. Nur, Darfiana |
| author_facet | Thi Thu Pham, H. Pham, H. Nur, Darfiana |
| author_sort | Thi Thu Pham, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Bayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on estimating parameters in a proposed fully Bayesian analysis setting for the penalized spline joint models. To achieve this aim, the joint posterior distribution of parameters in survival and longitudinal submodels is presented. The Markov chain Monte Carlo (MCMC) algorithm is then proposed, which consists of the Gibbs sampler (GS) and Metropolis Hastings (MH) algorithms to sample for the target conditional posterior distributions. The prior sensitivity analysis for the baseline hazard rate and association parameters is performed through simulation studies and a case study. |
| first_indexed | 2025-11-14T11:28:55Z |
| format | Journal Article |
| id | curtin-20.500.11937-88546 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:28:55Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-885462022-06-13T04:45:34Z A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis Thi Thu Pham, H. Pham, H. Nur, Darfiana Bayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on estimating parameters in a proposed fully Bayesian analysis setting for the penalized spline joint models. To achieve this aim, the joint posterior distribution of parameters in survival and longitudinal submodels is presented. The Markov chain Monte Carlo (MCMC) algorithm is then proposed, which consists of the Gibbs sampler (GS) and Metropolis Hastings (MH) algorithms to sample for the target conditional posterior distributions. The prior sensitivity analysis for the baseline hazard rate and association parameters is performed through simulation studies and a case study. 2020 Journal Article http://hdl.handle.net/20.500.11937/88546 10.1515/mcma-2020-2058 restricted |
| spellingShingle | Thi Thu Pham, H. Pham, H. Nur, Darfiana A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis |
| title | A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis |
| title_full | A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis |
| title_fullStr | A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis |
| title_full_unstemmed | A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis |
| title_short | A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis |
| title_sort | bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: a prior sensitivity analysis |
| url | http://hdl.handle.net/20.500.11937/88546 |