A Monte Carlo EM algorithm for random-coefficient-based dropout models


Verzilli, CJ; Carpenter, JR; (2002) A Monte Carlo EM algorithm for random-coefficient-based dropout models. Journal of applied statistics, 29 (7). pp. 1011-1021. ISSN 0266-4763 DOI: https://doi.org/10.1080/0266476022000006711

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Abstract

Longitudinal studies of neurological disorders suffer almost inevitably from non-compliance, which is likely to be non-ignorable. It is important in these cases to model the response variable and the dropout mechanism jointly. In this article we propose a Monte Carlo version of the EM algorithm that can be used to fit random-coefficient-based dropout models. A linear mixed model is assumed for the response variable and a discrete-time proportional hazards model for the dropout mechanism; these share a common set of random coefficients. The ideas are illustrated using data from a five-year trial assessing the efficacy of two drugs in the treatment of patients in the early stages of Parkinson's disease.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Web of Science ID: 177801700006
URI: http://researchonline.lshtm.ac.uk/id/eprint/6338

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