Robust joint modelling of longitudinal and survival data in a competing risk setting

Dr Freedom Gumedze will present the Department of Statistical Science seminar with a talk entitled, "Robust joint modelling of longitudinal and survival data in a competing risk setting".

Joint models for longitudinal data and competing risk survival data usually assume Gaussian random errors for the longitudinal sub-model which is not robust to outliers. This paper proposes a joint model for the analysis of longitudinal data and clustered competing risk survival data which downweights outliers or outlying subject profiles with respect to the longitudinal measurements. Our proposed model consists of a linear mixed effects sub-model for the longitudinal measurements and a proportional cause-specific hazard frailty sub-model, linked together by latent random effects. The proposed linear mixed effects sub-model considers an outlying subject as a subject whose profile has an inflated random effects variance-covariance matrix. A measure of the shift or inflation in the random effects variance-covariance matrix for a subject gives an indication of the outlyingness of that subject. We use a likelihood ratio test statistic to determine whether the ith subject has an inflated variance-covariance matrix and is therefore a possible outlier. The joint model can then be fitted to downweight the subject in the analysis, if desired. The proposed methodology is illustrated using a real dataset from TB pericarditis multicentre clinical trial.


Mon, 15 Aug 2016 - 13:00

PD Hahn Lecture Theatre 3, Upper Campus, UCT