ESTIMATING MEAN SURVIVAL TIME: WHEN IS IT POSSIBLE?
ESTIMATING MEAN SURVIVAL TIME: WHEN IS IT POSSIBLE?
复制标题
估计平均生存时间：什么时候可以？
DOI：
10.1111/sjos.12112
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Nan,Bin
中科院分区：
文献类型：
--
作者：
Ding,Ying;Nan,Bin
对于右删失生存数据，众所周知，当删失时间的支持包含生存时间的支持时，平均生存时间可以一致估计。然而，在实践中，这一条件很容易被违反，因为研究的随访通常在有限的窗口内。在这篇文章中，我们证明了当一些具有非零系数的协变量的支持度是无界的时，无论随访时间长短，平均生存时间仍然可以从线性模型中估计。这意味着在实际应用中，当线性预测因子的支持度较宽时，可以很好地估计平均生存时间。有限样本的仿真研究进一步验证了理论发现。模拟还表明，当两种模型都被正确指定时，线性模型产生合理的均方预测误差，并且优于考克斯模型，特别是在严重删失和随访时间短的情况下。
For right‐censored survival data, it is well‐known that the mean survival time can be consistently estimated when the support of the censoring time contains the support of the survival time. In practice, however, this condition can be easily violated because the follow‐up of a study is usually within a finite window. In this article, we show that the mean survival time is still estimable from a linear model when the support of some covariate(s) with non‐zero coefficient(s) is unbounded regardless of the length of follow‐up. This implies that the mean survival time can be well estimated when the support of linear predictor is wide in practice. The theoretical finding is further verified for finite samples by simulation studies. Simulations also show that, when both models are correctly specified, the linear model yields reasonable mean square prediction errors and outperforms the Cox model, particularly with heavy censoring and short follow‐up time.