Amy Laird
Longitudinal studies are a useful tool for investigating the course of chronic diseases. Many chronic diseases are progressive and can be characterized by a set of health states. We can gain a greater understanding of the disease process by modeling the sequence of visited states and the length of time spent in each state. The major modeling challenge is that the transition times are not known exactly under panel observation. Existing modeling approaches either impose strong parametric assumptions on the sojourn times in each state, or model time discretely and carry out inference nonparametrically, but both approaches have drawbacks. We propose an alternative modeling approach that uses the principle of data augmentation. This method has several advantages: (1) it accommodates any parametric model for the sojourn times, including spline models; (2) it performs well under small sample sizes for suitable parametric choices for the sojourn time distributions; and (3) it does not require that subjects be observed in every health state. We evaluate the performance of this approach under various choices of sojourn time model and compare it to existing approaches under a sparse observation scheme. Finally, we illustrate the proposed approach in an application and discuss future research directions.
