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

Claudio Verzilli and James Carpenter

Journal of Applied Statistics, 2002, vol. 29, issue 7, 1011-1021

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.

Date: 2002
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DOI: 10.1080/0266476022000006711

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