Wang Lianming, Sun Jianguo, Tong Xingwei
Biostatistics Branch, National Institute of Environmental Health Sciences, MD A3-03, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
Lifetime Data Anal. 2008 Jun;14(2):134-53. doi: 10.1007/s10985-007-9058-9.
We consider efficient estimation of regression and association parameters jointly for bivariate current status data with the marginal proportional hazards model. Current status data occur in many fields including demographical studies and tumorigenicity experiments and several approaches have been proposed for regression analysis of univariate current status data. We discuss bivariate current status data and propose an efficient score estimation approach for the problem. In the approach, the copula model is used for joint survival function with the survival times assumed to follow the proportional hazards model marginally. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. A real life data application is provided for illustration.
我们考虑在边际比例风险模型下，对双变量当前状态数据的回归和关联参数进行联合有效估计。当前状态数据出现在包括人口统计学研究和致癌性实验在内的许多领域，并且已经提出了几种用于单变量当前状态数据回归分析的方法。我们讨论双变量当前状态数据，并针对该问题提出一种有效的得分估计方法。在该方法中，将copula模型用于联合生存函数，假设生存时间边际上遵循比例风险模型。进行了模拟研究以评估所提出的估计量，并表明该方法在实际情况中效果良好。还提供了一个实际数据应用示例进行说明。
