Testing and interval estimation for two-sample survival comparisons with small sample sizes and unequal censoring
Testing and interval estimation for two-sample survival comparisons with small sample sizes and unequal censoring
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DOI：
10.1093/biostatistics/kxq021
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发表时间：
2010-10-01
期刊：
影响因子：
2.1
通讯作者：
Gray, Robert J.
中科院分区：
文献类型：
--
作者：
Wang, Rui;Lagakos, Stephen W.;Gray, Robert J.
虽然用于比较两组之间生存时间的常用对数秩检验具有许多理想的特性，但有时当样本量较小时，对数秩检验及其相关的线性秩检验表现不佳。类似的担忧也适用于这种情况下治疗差异的区间估计，尽管它们的特性不太为人所知。标准排列检验是一种选择，但当比较组中的基本审查分布不相等时，这些通常无效。我们开发了两种测试和区间估计方法，用于小样本和可能不等的审查，首先估算生存和审查时间，然后应用排列方法。其中之一为 Heinze 等人最近提出的方法提供了启发式的理由（2003 年，不平等后续行动的精确对数秩检验。Biometrics 59, 1151-1157）。仿真研究表明，所提出的方法具有良好的 I 类误差和功耗特性。对于加速失效时间模型，与 Jin 等人的渐近方法（2003，Rank-based inference for the Accelerated Failure Time Model. Biometrika 90, 341-353）相比，所提出的方法产生的置信区间在小样本设置中具有更好的覆盖概率，在样本量较大时具有相似的效率。所提出的方法通过癌症研究和艾滋病临床试验的数据进行了说明。
While the commonly used log-rank test for comparing survival times between 2 groups enjoys many desirable properties, sometimes the log-rank test and its related linear rank tests perform poorly when sample sizes are small. Similar concerns apply to interval estimates for treatment differences in this setting, though their properties are less well known. Standard permutation tests are one option, but these are not in general valid when the underlying censoring distributions in the comparison groups are unequal. We develop 2 methods for testing and interval estimation, for use with small samples and possibly unequal censoring, based on first imputing survival and censoring times and then applying permutation methods. One provides a heuristic justification for the approach proposed recently by Heinze and others (2003, Exact log-rank tests for unequal follow-up. Biometrics 59, 1151-1157). Simulation studies show that the proposed methods have good Type I error and power properties. For accelerated failure time models, compared to the asymptotic methods of Jin and others (2003, Rank-based inference for the accelerated failure time model. Biometrika 90, 341-353), the proposed methods yield confidence intervals with better coverage probabilities in small-sample settings and similar efficiency when sample sizes are large. The proposed methods are illustrated with data from a cancer study and an AIDS clinical trial.