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...
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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/13373/1/Bayesian%20approach%20for%20joint%20longitudinal%20and%20time.pdf http://psasir.upm.edu.my/id/eprint/13373/ http://www.emis.de/journals/BMMSS/vol32_1.htm |
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my.upm.eprints.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 |
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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. |
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Article |
author |
Abu Bakar, Mohd Rizam Salah, Khalid Ali Ibrahim, Noor Akma Haron, Kassim |
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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 |
author_facet |
Abu Bakar, Mohd Rizam Salah, Khalid Ali Ibrahim, Noor Akma Haron, Kassim |
author_sort |
Abu Bakar, Mohd Rizam |
title |
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_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_sort |
bayesian approach for joint longitudinal and time-to-event data with survival fraction |
publisher |
Malaysian Mathematical Sciences Society and Universiti Sains Malaysia |
publishDate |
2009 |
url |
http://psasir.upm.edu.my/id/eprint/13373/1/Bayesian%20approach%20for%20joint%20longitudinal%20and%20time.pdf http://psasir.upm.edu.my/id/eprint/13373/ http://www.emis.de/journals/BMMSS/vol32_1.htm |
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13.160551 |