Goggins W B, Finkelstein D M, Schoenfeld D A, Zaslavsky A M
Biostatistics Department, Harvard School of Public Health, Boston, Massachussetts 02115, USA.
Biometrics. 1998 Dec;54(4):1498-507.
This paper proposes a Monte Carlo EM (MCEM) algorithm for fitting the proportional hazards model for interval-censored failure-time data. The algorithm generates orderings of the failures from their probability distribution under the model. We maximize the average of the log-likelihoods from these completed data sets to obtain updated parameter estimates. As with the standard Cox model, this algorithm does not require the estimation of the baseline hazard function. The performance of the algorithm is evaluated using simulations, and the method is applied to data from AIDS and cancer studies. Our results indicate that our method produced more precise and unbiased estimates than methods of right and midpoint imputation.
本文提出了一种蒙特卡罗期望最大化（MCEM）算法，用于对区间删失失效时间数据拟合比例风险模型。该算法根据模型下失效的概率分布生成失效的排序。我们最大化这些完整数据集的对数似然的平均值，以获得更新的参数估计值。与标准的Cox模型一样，该算法不需要估计基线风险函数。通过模拟评估了该算法的性能，并将该方法应用于艾滋病和癌症研究的数据。我们的结果表明，与右删失和中点插补方法相比，我们的方法产生了更精确和无偏的估计值。
