Refreshments: 15.30 in MDS 312

Students visit: 15:00 in MDS 312

Abstract: 

Traditional methods for the analysis of failure time data are often employed in the marginal analysis of waiting times from multistate models. However, such methods can exhibit substantial bias when transition times between model states are dependent, even when censoring is independent. We introduce a nonparametric, inverse probability of censoring–weighted (IPCW) linear hazard model for waiting times from multistate models, analogous to Aalen’s linear hazard model for failure time data. We provide a weak convergence result for the IPCW regression coefficient estimator and illustrate its unbiasedness through a simulation study, while also demonstrating the bias of the traditional linear hazard model for failure time data when waiting times are correlated. The IPCW estimators are used to examine prognostic indicators for patients receiving bone marrow transplant and predictors of ambulatory recovery in a data set of incomplete spinal cord injury patients receiving activity-based rehabilitation. This is joint work with Doug Lorenz.