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Nonparametric Estimation of Event Probabilities for Non-Markov Multistate Event Data

David Glidden, Department of Biostatistics, University of California-San Francisco

Multistate event data, in which a single subject is at risk for multiple events, is common in biomedical applications. I consider nonparametric estimation of the vector of probabilities of state membership at time t. Estimators, which Aalen and Johansen (1978) derived under the Markov assumption, are shown to be consistent and asymptotically Gaussian for data which is non-Markov. In addition, procedures for confidence bands are derived in this general setting. The method is evaluated via simulation and applied to data from two clinical trials.


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