Statistical methods for complex clinical and survey data

这项拟议的研究的目标是开发分析复杂临床和调查数据的创新方法。在临床研究中，我们经常观察到具有复杂相关性结构的纵向和事件间隔时间数据，这些数据具有许多重要问题，包括脆弱性、具有检测限(LOD)的测量、时间相关协变量以及后续测量中的异常值。纵向和生存数据的联合模型通常用于关注生存时间，并且希望调查内生的时间依赖的协变量对患者生存的影响。我将通过解决上述生存分析的复杂问题来开发和探索有效的方法。具体地说，我将研究在联合分析纵向数据和事件间隔时间数据时，可以限制离群值影响的稳健方法。

The proposed research has the goal of developing innovative methods for analyzing complex clinical and survey data. In clinical studies, we often observe longitudinal and time-to-event data with complex dependence structures featuring many important issues including frailties, measurements with the limit of detection (LOD), time-dependent covariates, and outliers in follow-up measurements. Joint models for longitudinal and survival data are typically used when the focus is on the survival times and one wishes to investigate the effect of an endogenous time-dependent covariate on the survival of patients. I will develop and explore efficient methods by addressing the aforementioned complex issues of survival analysis. Specifically, I will investigate robust methods that can bound the influence of outliers when jointly analyzing the longitudinal and time-to-event data.