Flexible semi-parametric regression of state occupational probabilities in a multistate model with right-censored data
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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.
KeywordsBinary choice single index model Bone marrow transplant Multistate models Right-censoring Spinal code injury State occupational probabilities
We thank the Christopher and Dana Reeve Foundation and all current and past members of the NeuroRecovery Network for the provision of the spinal cord injury data. Also, we thank the associate editor and two anonymous reviewers for many constructive suggestions.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no competing interests.
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