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