Modelling recall-based competing risks data with k different models
In competing risks model, subjects are exposed to failure due to more than one cause. In this study, we develop a model for recall-based competing risks data when the causes associated with time to event are assumed to follow different distributions respectively. The chances of an individual recalli...
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| Veröffentlicht in: | Journal of statistical computation and simulation Jg. 94; H. 4; S. 722 - 743 |
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Taylor & Francis
03.03.2024
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| Abstract | In competing risks model, subjects are exposed to failure due to more than one cause. In this study, we develop a model for recall-based competing risks data when the causes associated with time to event are assumed to follow different distributions respectively. The chances of an individual recalling an event will be high if elapsed time between monitoring time and time to event is less. This information is utilized by taking non-recall probability as a function of this elapsed time. For point estimation, an expectation-maximization based algorithm is developed. For interval estimation, we construct the observed Fisher information matrix by using the missing information principle. The study is further extended to the Bayesian paradigm under suitable choices of prior distributions. The samples from full conditionals are drawn using a Gibbs sampling-based algorithm. To assess the performance of proposed estimators, an extensive simulation study is carried out for different proportions of non-recall and censored data with varying sample sizes under uniform and exponential monitoring patterns. Finally, the median duration of breastfeeding cessation for US women is estimated. |
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| AbstractList | In competing risks model, subjects are exposed to failure due to more than one cause. In this study, we develop a model for recall-based competing risks data when the causes associated with time to event are assumed to follow different distributions respectively. The chances of an individual recalling an event will be high if elapsed time between monitoring time and time to event is less. This information is utilized by taking non-recall probability as a function of this elapsed time. For point estimation, an expectation-maximization based algorithm is developed. For interval estimation, we construct the observed Fisher information matrix by using the missing information principle. The study is further extended to the Bayesian paradigm under suitable choices of prior distributions. The samples from full conditionals are drawn using a Gibbs sampling-based algorithm. To assess the performance of proposed estimators, an extensive simulation study is carried out for different proportions of non-recall and censored data with varying sample sizes under uniform and exponential monitoring patterns. Finally, the median duration of breastfeeding cessation for US women is estimated. |
| Author | Panwar, M. S. Yadav, C. P. |
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| Cites_doi | 10.1080/02664763.2018.1561833 10.1016/j.ress.2005.12.010 10.1093/biomet/asq083 10.2307/2530599 10.1016/S0169-7161(03)23035-2 10.1109/TR.2016.2575439 10.1109/24.814520 10.1080/00949655.2011.590808 10.1007/s13571-018-0172-3 10.1016/j.cam.2019.112537 10.1109/24.9880 10.1109/TR.24 10.1109/IRI-05.2005.1506479 10.1007/BF00128570 10.1007/s10985-006-9019-8 10.1201/9781420035902 10.1007/978-1-4684-6316-3_8 10.1111/j.1651-2227.2003.tb00501.x 10.1007/0-387-37119-2 10.1111/j.2517-6161.1977.tb01600.x 10.1142/S0218539397000035 10.1111/sjos.v42.1 10.1093/oxfordjournals.aje.a113548 10.1007/s13198-013-0191-4 10.1016/S0167-7152(99)00160-1 10.1080/02664763.2022.2052821 10.1080/01621459.1980.10477495 10.2307/2061354 10.1016/j.ress.2015.07.007 10.1111/j.2517-6161.1982.tb01203.x 10.1016/S0951-8320(03)00125-X 10.1080/10618600.1999.10474802 |
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| Title | Modelling recall-based competing risks data with k different models |
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