Robust inference for event probabilities with non-Markov event data.
Multistate event data, in which a single subject is at risk for multiple events, is common in biomedical applications. This article considers nonparametric estimation of the vector of probabilities of state membership at time t. Estimators, derived under the Markov assumption, have been shown (Datta and Satten, 2001, Statistics and Probability Letters 55, 403-411) to be consistent for data that is non-Markov. Inference, however, must take into account possibly non-Markov transitions when constructing confidence bands for event curves. We develop robust confidence bands for these curves, evaluate them via simulation, and illustrate the method on two datasets.
Glidden
David V
DV
Department of Epidemiology and Biostatistics, University of California, San Francisco 94143-0560, USA. david@biostat.ucsf.edu
D017088:AIDS-Related Opportunistic Infections / Q000517:prevention & control; D001699:Biometry; D016026:Bone Marrow Transplantation; D003627:Data Interpretation, Statistical; D018572:Disease-Free Survival; D006801:Humans; D007938:Leukemia / Q000628:therapy; D008390:Markov Chains; D015233:Models, Statistical; D011020:Pneumonia, Pneumocystis / Q000517:prevention & control; D011338:Probability Theory; D012008:Recurrence