Abstract #301102

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JSM 2003 Abstract #301102
Activity Number: 96
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301102
Title: Statistical Methods for Periodic Observations from a Semi-Markov Process, with Applications to HPV
Author(s): Minhee Kang*+ and Stephen W. Lagakos
Companies: Harvard School of Public Health and Harvard School of Public Health
Address: Dept. of Biostatistics, Boston, MA, 02115-6028,
Keywords: semi-Markov process ; panel data ; natural history ; misclassification
Abstract:

The study of some multistate disease processes can be complicated, due to the silent nature of state transitions that prevents individual patient histories from being fully observed. Observations consist of the state of the process at several prespecified time points that do not, in general, correspond to state transition times. Inference methods for such panel data have been developed for time-homogeneous Markov models (Kalbfleisch and Lawless 1985), but there has been little research done for panel data arising from other models. Motivated by the natural history of human papillomavirus (HPV) in women, we develop likelihood-based methods for panel data when the underlying process is a continuous-time, semi-Markov process, where there can be misclassification of states. Analytic expressions of likelihood contributions are shown to be greatly simplified when transition intensities from at least one of the states are time-independent. We illustrate the methods with a 4-state model and apply the results to placebo data from a recent clinical trial for the prevention of HPV.

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