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Repeated measures of biomarkers have the potential of explaining hazards ofsurvival outcomes. In practice, these measurements are intermittently measuredand are known to be subject to substantial measurement error. Joint modellingof longitudinal and survival data enables us to associate intermittentlymeasured error-prone biomarkers with risks of survival outcomes. Most of thejoint models available in the literature have been built on the Gaussianassumption. This makes them sensitive to outliers. In this work, we study arange of robust models to address this issue. For medical data, it has beenobserved that outliers might occur with different frequencies over time. Toaddress this, a new model with a time varying robustness is introduced. Throughboth a simulation study and analysis of two real-life data examples, thisresearch not only stresses the need to account for longitudinal outliers injoint modelling research but also highlights the bias and inefficiency from notproperly estimating the degrees-of-freedom parameter. Each technique presentedin this work can be fitted using the R package robjm.

Robust joint modelling of longitudinal and survival data with a time-varying degrees-of-freedom parameter