Taylor J M
Department of Biostatistics, University of California, Los Angeles 90024-1772, USA.
Biometrics. 1995 Sep;51(3):899-907.
A mixture model is an attractive approach for analyzing failure time data in which there are thought to be two groups of subjects, those who could eventually develop the endpoint and those who could not develop the endpoint. The proposed model is a semi-parametric generalization of the mixture model of Farewell (1982). A logistic regression model is proposed for the incidence part of the model, and a Kaplan-Meier type approach is used to estimate the latency part of the model. The estimator arises naturally out of the EM algorithm approach for fitting failure time mixture models as described by Larson and Dinse (1985). The procedure is applied to some experimental data from radiation biology and is evaluated in a Monte Carlo simulation study. The simulation study suggests the semi-parametric procedure is almost as efficient as the correct fully parametric procedure for estimating the regression coefficient in the incidence, but less efficient for estimating the latency distribution.
混合模型是一种用于分析失效时间数据的有吸引力的方法，其中假设有两组受试者，一组是最终可能发生终点事件的，另一组是不会发生终点事件的。所提出的模型是Farewell（1982）混合模型的半参数推广。针对模型的发生率部分提出了一个逻辑回归模型，并使用Kaplan-Meier类型的方法来估计模型的潜伏期部分。该估计器自然地源于Larson和Dinse（1985）所描述的用于拟合失效时间混合模型的EM算法方法。该程序应用于一些来自放射生物学的实验数据，并在蒙特卡罗模拟研究中进行评估。模拟研究表明，对于估计发生率中的回归系数，半参数程序几乎与正确的全参数程序一样有效，但在估计潜伏期分布方面效率较低。
