A dynamic Mover–Stayer model for recurrent event processes subject to resolution
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Date
2014-07
Authors
Cook, Richard J.
Shen, Hua
Journal Title
Journal ISSN
Volume Title
Publisher
Springer US
Abstract
In studies of affective disorder, individuals are often observed to experience recurrent symptomatic
exacerbations warranting hospitalization. Interest may lie in modeling the occurrence
of such exacerbations over time and identifying associated risk factors. In some patients, recurrent
exacerbations are temporally clustered following disease onset, but cease to occur after a
period of time.We develop a dynamic Mover-Stayer model in which a canonical binary variable
associated with each event indicates whether the underlying disease has resolved. An individual
whose disease process has not resolved will experience events following a standard point process
model governed by a latent intensity. When the disease process resolves, the complete data intensity
becomes zero and no further event will occur. An expectation- maximization algorithm
is described for parametric and semiparametric model fitting based on a discrete time dynamic
Mover-Stayer model and a latent intensity-based model of the underlying point process.
Description
The final publication is available at Springer via http://dx.doi.org/10.1007/s10985-013-9271-7
Keywords
Dynamic Mover-Stayer model, EM algorithm, Recurrent event data, Semiparametric estimation