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Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification

机译：误分类的区间删失时间数据的非参数最大似然估计

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摘要

The paper considers non-parametric maximum likelihood estimation of the failure time distribution for interval-censored data subject to misclassification. Such data can arise from two types of observation scheme; either where observations continue until the first positive test result or where tests continue regardless of the test results. In the former case, the misclassification probabilities must be known, whereas in the latter case, joint estimation of the event-time distribution and misclassification probabilities is possible. The regions for which the maximum likelihood estimate can only have support are derived. Algorithms for computing the maximum likelihood estimate are investigated and it is shown that algorithms appropriate for computing non-parametric mixing distributions perform better than an iterative convex minorant algorithm in terms of time to absolute convergence. A profile likelihood approach is proposed for joint estimation. The methods are illustrated on a data set relating to the onset of cardiac allograft vasculopathy in post-heart-transplantation patients.
机译：本文考虑了错误分类的区间删节数据的失效时间分布的非参数最大似然估计。这样的数据可以来自两种类型的观测方案：观察会一直持续到第一个阳性测试结果，或者无论测试结果如何都会继续进行测试。在前一种情况下，必须知道错误分类的概率，而在后一种情况下，可以对事件时间分布和错误分类的概率进行联合估计。得出最大似然估计只能获得支持的区域。研究了用于计算最大似然估计的算法，结果表明，就绝对收敛时间而言，适合于计算非参数混合分布的算法的性能优于迭代凸次要次要算法。提出了一种轮廓似然法用于联合估计。在与心脏移植后患者的心脏同种异体血管病发作有关的数据集上说明了这些方法。

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