Flexible semi-parametric regression of state occupational probabilities in a multistate model with right-censored data
Chathura Siriwardhana (),
K. B. Kulasekera () and
Somnath Datta ()
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Chathura Siriwardhana: University of Hawaii
K. B. Kulasekera: University of Louisville
Somnath Datta: University of Florida
Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 3, No 5, 464-491
Abstract Inference for the state occupation probabilities, given a set of baseline covariates, is an important problem in survival analysis and time to event multistate data. We introduce an inverse censoring probability re-weighted semi-parametric single index model based approach to estimate conditional state occupation probabilities of a given individual in a multistate model under right-censoring. Besides obtaining a temporal regression function, we also test the potential time varying effect of a baseline covariate on future state occupation. We show that the proposed technique has desirable finite sample performances and its performance is competitive when compared with three other existing approaches. We illustrate the proposed methodology using two different data sets. First, we re-examine a well-known data set dealing with leukemia patients undergoing bone marrow transplant with various state transitions. Our second illustration is based on data from a study involving functional status of a set of spinal cord injured patients undergoing a rehabilitation program.
Keywords: Binary choice single index model; Bone marrow transplant; Multistate models; Right-censoring; Spinal code injury; State occupational probabilities (search for similar items in EconPapers)
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