Bayesian approach for joint longitudinal and time-to-event data with survival fraction
Many medical investigations generate both repeatedly-measured(longitudinal) biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the surviv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Malaysian Mathematical Sciences Society and Universiti Sains Malaysia
2009
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| Online Access: | http://psasir.upm.edu.my/id/eprint/13373/ http://psasir.upm.edu.my/id/eprint/13373/1/Bayesian%20approach%20for%20joint%20longitudinal%20and%20time.pdf |
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| author | Abu Bakar, Mohd Rizam Salah, Khalid Ali Ibrahim, Noor Akma Haron, Kassim |
| author_facet | Abu Bakar, Mohd Rizam Salah, Khalid Ali Ibrahim, Noor Akma Haron, Kassim |
| author_sort | Abu Bakar, Mohd Rizam |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Many medical investigations generate both repeatedly-measured(longitudinal) biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model [11] is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper, we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will present, and for the survival model a semiparametric survival function will be considered which
accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the non-negativity of the survival function. A simulation study is presented to evaluate the performance of the proposed joint model. |
| first_indexed | 2025-11-15T07:53:41Z |
| format | Article |
| id | upm-13373 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T07:53:41Z |
| publishDate | 2009 |
| publisher | Malaysian Mathematical Sciences Society and Universiti Sains Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-133732015-11-19T08:17:17Z http://psasir.upm.edu.my/id/eprint/13373/ Bayesian approach for joint longitudinal and time-to-event data with survival fraction Abu Bakar, Mohd Rizam Salah, Khalid Ali Ibrahim, Noor Akma Haron, Kassim Many medical investigations generate both repeatedly-measured(longitudinal) biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model [11] is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper, we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will present, and for the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the non-negativity of the survival function. A simulation study is presented to evaluate the performance of the proposed joint model. Malaysian Mathematical Sciences Society and Universiti Sains Malaysia 2009 Article NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/13373/1/Bayesian%20approach%20for%20joint%20longitudinal%20and%20time.pdf Abu Bakar, Mohd Rizam and Salah, Khalid Ali and Ibrahim, Noor Akma and Haron, Kassim (2009) Bayesian approach for joint longitudinal and time-to-event data with survival fraction. Bulletin of the Malaysian Mathematical Sciences Society, 32 (1). pp. 75-100. ISSN 0126-6705; ESSN: 2180-4206 http://www.emis.de/journals/BMMSS/vol32_1.htm |
| spellingShingle | Abu Bakar, Mohd Rizam Salah, Khalid Ali Ibrahim, Noor Akma Haron, Kassim Bayesian approach for joint longitudinal and time-to-event data with survival fraction |
| title | Bayesian approach for joint longitudinal and time-to-event data with survival fraction |
| title_full | Bayesian approach for joint longitudinal and time-to-event data with survival fraction |
| title_fullStr | Bayesian approach for joint longitudinal and time-to-event data with survival fraction |
| title_full_unstemmed | Bayesian approach for joint longitudinal and time-to-event data with survival fraction |
| title_short | Bayesian approach for joint longitudinal and time-to-event data with survival fraction |
| title_sort | bayesian approach for joint longitudinal and time-to-event data with survival fraction |
| url | http://psasir.upm.edu.my/id/eprint/13373/ http://psasir.upm.edu.my/id/eprint/13373/ http://psasir.upm.edu.my/id/eprint/13373/1/Bayesian%20approach%20for%20joint%20longitudinal%20and%20time.pdf |