Competing risks survival data under middle censoring—An application to COVID-19 pandemic
Competing risks survival data under middle censoring—An application to COVID-19 pandemic
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DOI：
10.1016/j.health.2021.100006
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发表时间：
2021-09-28
期刊：
影响因子：
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通讯作者：
Jammalamadaka SR
中科院分区：
文献类型：
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作者：
Rehman H;Chandra N;Jammalamadaka SR
在竞争风险下使用特定分位数函数建模，在中间删失方案下分析生存数据。在COVID-19大流行的情况下，使用中间删失方案已被证明是非常合适的。在中间截尾下的原因特定的分位数推断。这种分位数推断是通过基于特定原因比例风险模型的累积发生率函数获得的。假设基线寿命遵循非常一般的参数模型，即威布尔分布，并且与删失机制无关。我们获得的未知参数的估计，并导致特定的分位数函数下的经典以及贝叶斯设置。蒙特卡洛模拟研究评估不同的估计的相对性能。最后，一个真实的寿命数据分析的说明所提出的方法。
Survival data is being analysed here under the middle censoring scheme, using specifically quantile function modelling under competing risks. The use of middle censoring scheme has been shown to be very appropriate under the COVID-19 pandemic scenario. Cause-specific quantile inference under middle censoring is employed. Such quantile inferences are obtained through cumulative incidence function based on cause-specific proportional hazards model. The baseline lifetime is assumed to follow a very general parametric model namely the Weibull distribution, and is independent of the censoring mechanism. We obtain estimates of the unknown parameters and cause specific quantile functions under classical as well as a Bayesian set-up. A Monte Carlo simulation study assesses the relative performance of the different estimators. Finally, a real life data analysis is given for illustration of the proposed methods.