Wahed Abdus S, Tsiatis Anastasios A
Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania 15261, USA.
Biometrics. 2004 Mar;60(1):124-33. doi: 10.1111/j.0006-341X.2004.00160.x.
Two-stage designs, where patients are initially randomized to an induction therapy and then depending upon their response and consent, are randomized to a maintenance therapy, are common in cancer and other clinical trials. The goal is to compare different combinations of primary and maintenance therapies to find the combination that is most beneficial. In practice, the analysis is usually conducted in two separate stages which does not directly address the major objective of finding the best combination. Recently Lunceford, Davidian, and Tsiatis (2002, Biometrics58, 48-57) introduced ad hoc estimators for the survival distribution and mean restricted survival time under different treatment policies. These estimators are consistent but not efficient, and do not include information from auxiliary covariates. In this article we derive estimators that are easy to compute and are more efficient than previous estimators. We also show how to improve efficiency further by taking into account additional information from auxiliary variables. Large sample properties of these estimators are derived and comparisons with other estimators are made using simulation. We apply our estimators to a leukemia clinical trial data set that motivated this study.
两阶段设计在癌症和其他临床试验中很常见，即患者首先被随机分配接受诱导治疗，然后根据其反应和同意情况，再被随机分配接受维持治疗。目的是比较初始治疗和维持治疗的不同组合，以找到最有益的组合。在实际操作中，分析通常在两个独立阶段进行，这并未直接解决寻找最佳组合这一主要目标。最近，伦瑟福德、戴维迪安和齐亚蒂斯（2002年，《生物统计学》58卷，48 - 57页）针对不同治疗策略下的生存分布和平均受限生存时间引入了特别估计量。这些估计量是一致的，但效率不高，且未包含来自辅助协变量的信息。在本文中，我们推导出易于计算且比先前估计量更有效的估计量。我们还展示了如何通过考虑来自辅助变量的额外信息进一步提高效率。推导了这些估计量的大样本性质，并通过模拟与其他估计量进行比较。我们将我们的估计量应用于激发本研究的白血病临床试验数据集。
