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

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  • Claudio Verzilli
  • James Carpenter

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.

Suggested Citation

  • Claudio Verzilli & James Carpenter, 2002. "A Monte Carlo EM algorithm for random-coefficient-based dropout models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 1011-1021.
    • Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:1011-1021
    DOI: 10.1080/0266476022000006711
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    References listed on IDEAS

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    1. Quintana, Fernando A. & Liu, Jun S. & Pino, Guido E. del, 1999. "Monte Carlo EM with importance reweighting and its applications in random effects models," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 429-444, February.
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    Cited by:

    1. Chan, Jennifer S.K. & Leung, Doris Y.P. & Boris Choy, S.T. & Wan, Wai Y., 2009. "Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4530-4545, October.

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