A semiparametric mixture cure survival model for left‐truncated and right‐censored data
A semiparametric mixture cure survival model for left‐truncated and right‐censored data
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DOI：
10.1002/bimj.201500267
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发表时间：
2017-03
影响因子：
1.7
通讯作者：
Chyong-Mei Chen;P. Shen;J. Wei;Lichi Lin
中科院分区：
文献类型：
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作者：
Chyong-Mei Chen;P. Shen;J. Wei;Lichi Lin
在随访研究中，疾病事件时间可进行左截断和右删失。此外，医学的进步使某些类型的疾病有可能被治愈。在这篇文章中，我们考虑了一个半参数混合治愈模型，用于左截断右删失数据的回归分析。该模型将事件发生概率的逻辑回归与发生时间的转换模型类相结合。我们研究了两种估计模型参数的技术。第一种方法是基于鞅估计方程（EEs）。第二种方法是基于给定截断变量的条件似然函数。这两个估计的渐近性质的建立。模拟研究表明，条件最大似然估计（cMLE）表现良好，而基于EE的估计非常不稳定，即使它被证明是一致的。这是一个特殊的和有趣的现象下治愈模型的EE方法。我们提供了这个问题的见解，并发现EE的方法可以显着改善分配适当的权重，在EE的删失观测。这一发现是有用的，在克服不稳定的EE方法在一些更复杂的情况下，可能性的方法是不可行的。我们通过分析强直性脊柱炎患者枕壁距离事件发生时的年龄来说明所提出的估计程序。
In follow‐up studies, the disease event time can be subject to left truncation and right censoring. Furthermore, medical advancements have made it possible for patients to be cured of certain types of diseases. In this article, we consider a semiparametric mixture cure model for the regression analysis of left‐truncated and right‐censored data. The model combines a logistic regression for the probability of event occurrence with the class of transformation models for the time of occurrence. We investigate two techniques for estimating model parameters. The first approach is based on martingale estimating equations (EEs). The second approach is based on the conditional likelihood function given truncation variables. The asymptotic properties of both proposed estimators are established. Simulation studies indicate that the conditional maximum‐likelihood estimator (cMLE) performs well while the estimator based on EEs is very unstable even though it is shown to be consistent. This is a special and intriguing phenomenon for the EE approach under cure model. We provide insights into this issue and find that the EE approach can be improved significantly by assigning appropriate weights to the censored observations in the EEs. This finding is useful in overcoming the instability of the EE approach in some more complicated situations, where the likelihood approach is not feasible. We illustrate the proposed estimation procedures by analyzing the age at onset of the occiput‐wall distance event for patients with ankylosing spondylitis.