Cumulative Hazard Ratio Estimation for Treatment Regimes in Sequentially Randomized Clinical Trials.
Cumulative Hazard Ratio Estimation for Treatment Regimes in Sequentially Randomized Clinical Trials.
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DOI：
10.1007/s12561-013-9089-6
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发表时间：
2015-05
影响因子：
1
通讯作者：
Wahed, Abdus S
中科院分区：
文献类型：
--
作者：
Tang, Xinyu;Wahed, Abdus S
比例风险模型被广泛应用于生存分析中，以允许调整基线协变量。比例风险假设可能不适用于依赖于对先前接受治疗的中间反应的治疗方案，并且尚不清楚这种模型如何适用于采用一种以上随机化的临床试验。此外，由于治疗是在基线后修改的，风险不太可能在不同的治疗方案中成比例。尽管Lokhnygina和Helterbrand(Biometrics 63：422-428,2007)为两阶段随机设计引入了Cox回归方法，但他们的方法只能用于检验共享相同维持疗法的两个治疗方案的相等性。此外，他们的方法不允许在模型中包括辅助变量，也不考虑随时间变化不恒定的治疗效果。在这篇文章中，我们提出了一个模型，该模型假设每个治疗方案内的协变量之间的比例，但不是跨治疗方案。不同治疗方案之间的比较是通过检验估计的累积危险的对数比来进行的。使用加权Breslow类型的统计量来估计不同治疗方案的累积风险比率。为了评估估计器和拟议检验的性能，进行了模拟研究。
The proportional hazards model is widely used in survival analysis to allow adjustment for baseline covariates. The proportional hazard assumption may not be valid for treatment regimes that depend on intermediate responses to prior treatments received, and it is not clear how such a model can be adapted to clinical trials employing more than one randomization. Besides, since treatment is modified post-baseline, the hazards are unlikely to be proportional across treatment regimes. Although Lokhnygina and Helterbrand (Biometrics 63: 422–428, 2007) introduced the Cox regression method for two-stage randomization designs, their method can only be applied to test the equality of two treatment regimes that share the same maintenance therapy. Moreover, their method does not allow auxiliary variables to be included in the model nor does it account for treatment effects that are not constant over time. In this article, we propose a model that assumes proportionality across covariates within each treatment regime but not across treatment regimes. Comparisons among treatment regimes are performed by testing the log ratio of the estimated cumulative hazards. The ratio of the cumulative hazard across treatment regimes is estimated using a weighted Breslow-type statistic. A simulation study was conducted to evaluate the performance of the estimators and proposed tests.