A growth mixture Tobit model: application to AIDS studies

Getachew A. Dagne

Journal of Applied Statistics, 2016, vol. 43, issue 7, 1174-1185

Abstract: This paper presents an alternative analysis approach to modeling data where a lower detection limit (LOD) and unobserved population heterogeneity exist in a longitudinal data set. Longitudinal data on viral loads in HIV/AIDS studies, for instance, show strong positive skewness and left-censoring. Normalizing such data using a logarithmic transformation seems to be unsuccessful. An alternative to such a transformation is to use a finite mixture model which is suitable for analyzing data which have skewed or multi-modal distributions. There is little work done to simultaneously take into account these features of longitudinal data. This paper develops a growth mixture Tobit model that deals with a LOD and heterogeneity among growth trajectories. The proposed methods are illustrated using simulated and real data from an AIDS clinical study.

Date: 2016
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DOI: 10.1080/02664763.2015.1092114

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