Nonparametric estimation for length-biased and right-censored data
Nonparametric estimation for length-biased and right-censored data
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DOI：
10.1093/biomet/asq069
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发表时间：
2011-03-01
期刊：
影响因子：
2.7
通讯作者：
Qin, Jing
中科院分区：
文献类型：
--
作者：
Huang, Chiung-Yu;Qin, Jing
本文考虑了长度有偏抽样下的生存数据，其中生存时间被均匀分布的随机截尾时间截尾。我们提出了一个非参数估计，它包含了有关长度有偏抽样方案的信息。新的估计量保留了截断乘积限估计量的简洁性和封闭形式的表达式，并且与需要迭代算法的非参数极大似然估计量相比具有较小的效率损失。此外，所提出的估计量的渐近方差具有封闭形式，方差估计量很容易通过插入方法获得。实际样本量的数值模拟研究进行了比较所提出的方法与其竞争对手的性能。通过对加拿大健康与老龄化研究的数据分析，说明了该方法和理论。
This paper considers survival data arising from length-biased sampling, where the survival times are left truncated by uniformly distributed random truncation times. We propose a nonparametric estimator that incorporates the information about the length-biased sampling scheme. The new estimator retains the simplicity of the truncation product-limit estimator with a closed-form expression, and has a small efficiency loss compared with the nonparametric maximum likelihood estimator, which requires an iterative algorithm. Moreover, the asymptotic variance of the proposed estimator has a closed form, and a variance estimator is easily obtained by plug-in methods. Numerical simulation studies with practical sample sizes are conducted to compare the performance of the proposed method with its competitors. A data analysis of the Canadian Study of Health and Aging is conducted to illustrate the methods and theory.