Joint models for longitudinal and survival data with measurement errors, missing values, and outliers

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Joint modelling of longitudinal data and survival data has received great attention in the literature in recent years. Compared with commonly used naive approaches, these joint modelling methods can produce more efficient, less biased, and more robust parameter estimates. Mixed effects models are commonly used in the joint models where the longitudinal model and the survival model are linked through shared random effects. The likelihood method is a standard approach for estimation and inference for joint models. The major challenges for estimation and inference for joint models are: i) computation can be extremely demanding, because the likelihoods involve high-dimensional and intractable integrals. ii) missing data, measurement errors, and outliers are very common in longitudinal studies, and they lead to much greater difficulties in the computation; iii) user-friendly software is unavailable, which has been a major obstacle for applied statisticians and other researchers to use joint model in practise.