Measurement error problems in modeling multivariate failure time data

针对多元故障时间数据统计分析中的测量误差问题，提出了一种研究方案。当使用边际风险模型分析(相关)多变量故障时间数据时，我们将开发方法来纠正因协变量中的测量误差而导致的参数估计偏差。我们将开发一些方法来处理缺少协变量但带有辅助信息的问题。在缺失值的辅助信息已知的情况下，对广义线性混合模型的缺失值问题进行研究。我们将研究参数同位数的渐近性质。我们将对它们的小样本性质进行模拟研究。这些方法将被用来分析实际数据以进行说明。我们建议的研究结果将提供一种更有效的方法来分析来自医学和其他研究的数据。它可以帮助医学和其他研究人员更好地了解他们潜在的研究计划，最终可能导致更好的医疗方法和更有效的药物的设计。从而造福于加拿大人民和其他国家的人民。

We propose a research program on the measurement error problems in statistical analysis of multivariate failure time data. We will develop approaches to correct biases of parameter estimates induced by measurement error in covariates when using a marginal hazards model to analyze (correlated) multivariate failure time data. We will develop approaches to handle problems of missing covariates but with auxiliary information. We will conduct research work on missing value problems of generalized linear mixed models when auxiliary information to the missing values is available. We will investigate the asymptotic properties of the parameter esitmates. We will conduct simulation studies on small sample properties of them. The methods will be use to analyze real data for illustration. The outcome of our proposed research will provide a more efficient method to analyze data from medical and other studies. It can help medical and other investigators to better understand their underlying research program, that could ultimately lead to the design of better medical treatments and more efficient medicine. Thus, benefiting people of Canada and people of other countries.