Sample size calculation for a proportional hazards mixture cure model with nonbinary covariates
Sample size calculation for a proportional hazards mixture cure model with nonbinary covariates
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DOI：
10.1080/02664763.2018.1498463
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发表时间：
2019-01-01
影响因子：
1.5
通讯作者：
Hardin, James W.
中科院分区：
文献类型：
--
作者：
Zhan, Yihong;Zhang, Yanan;Hardin, James W.
样本量计算是临床试验中的一个关键问题，因为小样本量会导致推断偏差，大样本量会增加成本。随着先进医疗技术的发展，一些患者的某些慢性疾病可以被治愈，并且开发了比例风险混合治愈模型来处理具有潜在治愈信息的生存数据。考虑到具有潜在治愈比例的生存试验的需要，Wang等人提出了基于二元协变量对数秩检验统计量的相应样本量公式。 [25]。然而，基于连续变量的样本量公式尚未开发出来。在此，我们提出了基于对数秩方法的连续变量混合物固化模型的样本量和功效计算，并通过尤厄尔方法对其进行了进一步修改。使用指数分布和威布尔分布的合成数据的模拟研究来评估所提出的方法。在 R 中实现了计算混合固化模型中连续协变量所需样本量的程序。
Sample size calculation is a critical issue in clinical trials because a small sample size leads to a biased inference and a large sample size increases the cost. With the development of advanced medical technology, some patients can be cured of certain chronic diseases, and the proportional hazards mixture cure model has been developed to handle survival data with potential cure information. Given the needs of survival trials with potential cure proportions, a corresponding sample size formula based on the log-rank test statistic for binary covariates has been proposed by Wang et al. [25]. However, a sample size formula based on continuous variables has not been developed. Herein, we presented sample size and power calculations for the mixture cure model with continuous variables based on the log-rank method and further modified it by Ewell's method. The proposed approaches were evaluated using simulation studies for synthetic data from exponential and Weibull distributions. A program for calculating necessary sample size for continuous covariates in a mixture cure model was implemented in R.