A nonlinear mixed-effects model for multivariate longitudinal data with partially observed outcomes with application to HIV disease dynamics

• Published in: Journal of applied statistics Vol. 44; no. 3; pp. 441 - 456
• Main Authors: Luwanda, A. G., Mwambi, H. G.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 17.02.2017; Taylor & Francis Ltd
• Subjects: Approximation; HIV; HIV dynamical system; Human immunodeficiency virus; incomplete data; left-censored data; Mathematical analysis; Mathematical models; Monitors; Multivariate response; Nonlinear dynamics; Nonlinearity; Parameters; partial marker dropout; Progressions; Regression analysis; stochastic approximation expectation-maximisation algorithm; Stochastic models
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2016.1177494

The measurable multiple bio-markers for a disease are used as indicators for studying the response variable of interest in order to monitor and model disease progression. However, it is common for subjects to drop out of the studies prematurely resulting in unbalanced data and hence complicating the inferences involving such data. In this paper we consider a case where data are unbalanced among subjects and also within a subject because for some reason only a subset of the multiple outcomes of the response variable are observed at any one occasion. We propose a nonlinear mixed-effects model for the multivariate response variable data and derive a joint likelihood function that takes into account the partial dropout of the outcomes of the response variable. We further show how the methodology can be used in the estimation of the parameters that characterise HIV disease dynamics. An approximation technique of the parameters is also given and illustrated using a routine observational HIV dataset.