Statistical methods for complex clinical and survey data

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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.