论文标题
在竞争风险数据中使用限制的平均时间损失
The use of restricted mean time lost under competing risks data
论文作者
论文摘要
背景：在竞争风险下，常用的子分布危险比（SHR）并不容易在临床上解释，并且仅在比例子分布危险（SDH）假设下才有效。本文介绍了另一种统计措施：限制的平均损失（RMTL）。方法：首先，引入了措施的定义和估计方法。其次，基于RMTL的差异，基本差异测试（DIFF）和上皮差异测试（SDIFF）。然后，提出了相应的样本量估计方法。使用Monte Carlo模拟评估该方法和估计样本量的统计特性，这些方法也应用于两个真实示例。结果：仿真结果表明，在大多数情况下，SDIFF的性能很好，并且测试效率相对较高。关于样本量计算，SDIFF在各种情况下表现出良好的性能。使用两个示例说明了这些方法。结论：RMTL可以有意义地总结临床决策的治疗效果，然后可以用SDH比率进行竞争风险数据的SDH比率进行报告。拟议的SDIFF测试和两个计算的样本量公式具有广泛的适用性，可以在实际数据分析和试验设计中考虑。
Background: Under competing risks, the commonly used sub-distribution hazard ratio (SHR) is not easy to interpret clinically and is valid only under the proportional sub-distribution hazard (SDH) assumption. This paper introduces an alternative statistical measure: the restricted mean time lost (RMTL). Methods: First, the definition and estimation methods of the measures are introduced. Second, based on the differences in RMTLs, a basic difference test (Diff) and a supremum difference test (sDiff) are constructed. Then, the corresponding sample size estimation method is proposed. The statistical properties of the methods and the estimated sample size are evaluated using Monte Carlo simulations, and these methods are also applied to two real examples. Results: The simulation results show that sDiff performs well and has relatively high test efficiency in most situations. Regarding sample size calculation, sDiff exhibits good performance in various situations. The methods are illustrated using two examples. Conclusions: RMTL can meaningfully summarize treatment effects for clinical decision making, which can then be reported with the SDH ratio for competing risks data. The proposed sDiff test and the two calculated sample size formulas have wide applicability and can be considered in real data analysis and trial design.
