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Biostatistics Advance Access published online on June 1, 2006
Biostatistics, doi:10.1093/biostatistics/kxl006
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received October 9, 2005
Revised May 18, 2006
Accepted May 31, 2006

Article

Statistical methods for panel data from a semi-Markov process, with application to HPV

Minhee Kang 1 * and Stephen W. Lagakos 1

1 Department of Biostatistics, Harvard University School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115

* To whom correspondence should be addressed.
Minhee Kang, E-mail: mkang{at}hsph.harvard.edu


  Abstract

Continuous-time, multi-state processes can be used to represent a variety of biological processes in the public health sciences, yet the analysis of such processes is complex when they are observed only at a limited number of time points. Inference methods for such panel data have been developed for time-homogeneous Markov models, but there has been little research done for other classes of processes. We develop likelihood-based methods for panel data from a semi-Markov process, where transition intensities depend on the duration of time in the current state. The proposed methods account for possible misclassification of states. To illustrate the methods, we investigate a 3- and a 4-state models in detail and apply the results to model the natural history of oncogenic genital human papillomavirus (HPV) infections in women.

Keywords: Multi-state process; Misclassification; Natural history; Human papillomavirus.
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