Estimation of Multi-state Models with Missing Covariate Values Based on Observed Data Likelihood.

Continuous-time multi-state models are commonly used to study diseases with multiple stages. Potential risk factors associated with the disease are added to the transition intensities of the model as covariates, but missing covariate measurements arise frequently in practice. We propose a likelihood-based method that deals efficiently with a missing covariate in these models. Our simulation study showed that the method performs well for both 'missing completely at random' and 'missing at random' mechanisms. We also applied our method to a real dataset, the Einstein Aging Study.