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Joint modeling of longitudinal data and informative dropout time in the presence of multiple change points

Pulak Ghosh, Kaushik Ghosh & Ram C Tiwari`
Journal Name
Statistics in Medicine
Journal Publication
others
Publication Year
2011
Journal Publications Functional Area
Decision Sciences and Information Systems
Publication Date
Vol. 30, No. 6, PP 611-626, 15 March 2011
Abstract

In longitudinal studies of patients with the human immunodeficiency virus (HIV), objectives of interest often include modeling of individual-level trajectories of HIV ribonucleic acid (RNA) as a function of time. Such models can be used to predict the effects of different treatment regimens or to classify subjects into subgroups with similar trajectories. Empirical evidence, however, suggests that individual trajectories often possess multiple points of rapid change, which may vary from subject to subject.

Joint modeling of longitudinal data and informative dropout time in the presence of multiple change points

Author(s) Name: Pulak Ghosh, Kaushik Ghosh & Ram C Tiwari`
Journal Name: Statistics in Medicine
Volume: Vol. 30, No. 6, PP 611-626, 15 March 2011
Year of Publication: 2011
Abstract:

In longitudinal studies of patients with the human immunodeficiency virus (HIV), objectives of interest often include modeling of individual-level trajectories of HIV ribonucleic acid (RNA) as a function of time. Such models can be used to predict the effects of different treatment regimens or to classify subjects into subgroups with similar trajectories. Empirical evidence, however, suggests that individual trajectories often possess multiple points of rapid change, which may vary from subject to subject.