Zhang Jenny J, Wang Molin
Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA.
Biom J. 2009 Dec;51(6):932-45. doi: 10.1002/bimj.200800244.
We extend the Dahlberg and Wang (Biometrics 2007, 63, 1237-1244) proportional hazards (PH) cure model for the analysis of time-to-event data that is subject to a cure rate with masked event to a setting where the PH assumption does not hold. Assuming an accelerated failure time (AFT) model with unspecified error distribution for the time to the event of interest, we propose rank-based estimating equations for the model parameters and use a generalization of the EM algorithm for parameter estimation. Applying our proposed AFT model to the same motivating breast cancer dataset as Dahlberg and Wang (Biometrics 2007, 63, 1237-1244), our results are more intuitive for the treatment arm in which the PH assumption may be violated. We also conduct a simulation study to evaluate the performance of the proposed method.
我们将达尔伯格和王（《生物统计学》，2007年，第63卷，第1237 - 1244页）用于分析具有治愈率且事件被掩盖的生存时间数据的比例风险（PH）治愈模型扩展到比例风险假设不成立的情况。假设感兴趣事件发生时间的加速失效时间（AFT）模型具有未指定的误差分布，我们提出基于秩的估计方程来估计模型参数，并使用期望最大化（EM）算法的推广方法进行参数估计。将我们提出的AFT模型应用于与达尔伯格和王（《生物统计学》，2007年，第63卷，第1237 - 1244页）相同的激发性乳腺癌数据集，对于可能违反比例风险假设的治疗组，我们的结果更直观。我们还进行了模拟研究以评估所提方法的性能。
