Flexible Semiparametric Regression of State Occupational Probabilities in a Multistate Model with Right-Censored Data

• Activity Number: 230 - Longitudinal Data Analysis
• Type: Contributed
• Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Chathura Siriwardhana* and Karunarathna Kulasekera and Somnath Datta
• Companies: University of Hawaii and University of louisville and University of Florida
• Keywords: Binary choice single index model ; Bone marrow transplant ; Multistate models ; Right-censoring ; Spinal code injury ; State occupational probabilities

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 potentially time varying effects 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 two other existing approaches. We illustrate the proposed methodology using two different data sets. First, we re-examine a well known data set on various event times tracking the progression of a sample of leukemia patients undergoing bone marrow transplant. Our second illustration is based on the functional status of a set of spinal cord injured patients undergoing a rehabilitation program.