Children's Cancer Research Institute, A-1090, Vienna, Austria.
Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090, Vienna, Austria.
BMC Med Res Methodol. 2018 Jan 19;18(1):14. doi: 10.1186/s12874-017-0430-5.
Investigating the impact of a time-dependent intervention on the probability of long-term survival is statistically challenging. A typical example is stem-cell transplantation performed after successful donor identification from registered donors. Here, a suggested simple analysis based on the exogenous donor availability status according to registered donors would allow the estimation and comparison of survival probabilities. As donor search is usually ceased after a patient's event, donor availability status is incompletely observed, so that this simple comparison is not possible and the waiting time to donor identification needs to be addressed in the analysis to avoid bias. It is methodologically unclear, how to directly address cumulative long-term treatment effects without relying on proportional hazards while avoiding waiting time bias.
The pseudo-value regression technique is able to handle the first two issues; a novel generalisation of this technique also avoids waiting time bias. Inverse-probability-of-censoring weighting is used to account for the partly unobserved exogenous covariate donor availability.
Simulation studies demonstrate unbiasedness and satisfying coverage probabilities of the new method. A real data example demonstrates that study results based on generalised pseudo-values have a clear medical interpretation which supports the clinical decision making process.
The proposed generalisation of the pseudo-value regression technique enables to compare survival probabilities between two independent groups where group membership becomes known over time and remains partly unknown. Hence, cumulative long-term treatment effects are directly addressed without relying on proportional hazards while avoiding waiting time bias.
研究时间依赖性干预对长期生存概率的影响在统计学上具有挑战性。一个典型的例子是在成功从注册供体中识别出供体后进行的干细胞移植。在这里，根据注册供体的供体可用性状态提出一种简单的分析方法，可以估计和比较生存概率。由于在患者发病后通常会停止寻找供体，因此供体可用性状态无法完全观察到，因此无法进行这种简单的比较，并且需要在分析中解决寻找供体的等待时间问题，以避免偏差。在不依赖比例风险的情况下，如何直接解决累积长期治疗效果，同时避免等待时间偏倚，在方法学上尚不清楚。
伪值回归技术能够解决前两个问题；该技术的一种新推广也避免了等待时间偏差。逆概率删失加权用于解释部分未观察到的外生协变量供体可用性。
模拟研究证明了新方法的无偏性和令人满意的覆盖率概率。一个真实数据的例子表明，基于广义伪值的研究结果具有明确的医学解释，支持临床决策过程。
提出的伪值回归技术的推广可以比较两个独立组的生存概率，其中组归属随着时间的推移逐渐变得已知，但仍有部分未知。因此，在不依赖比例风险的情况下，直接解决累积长期治疗效果问题，同时避免等待时间偏倚。
