Inference of Transition Probabilities in Multi-State Models Using Adaptive Inverse Probability Censoring Weighting Technique

Inverse probability censoring weighting (IPCW) technique is often used to adjust for right censoring or recover information for censored individuals in survival analysis and in multi-state modeling. A simple IPCW (SIPCW) technique which does not consider the intermediate states, has been proposed for analyzing multi-state data. However, our simulation studies show that the SIPCW technique may lead to biased estimates when being applied in complex multi-state models. We thereby propose a model-specific, state-dependent adaptive IPCW (AIPCW) technique for estimating transition probabilities in multi-state models. Intensive simulation results verified that the proposed AIPCW technique improves the accuracy of transition probability estimates compared to the SIPCW technique and leads to asymptotic unbiased estimates. We applied the proposed technique to a real-world hematopoietic stem cell transplant (HSCT) data to assess the acute and chronic graft-versus-host disease (GVHD) effects on disease relapse rates and mortality rates.