Estimating transition probabilities from aggregate samples plus partial transition data.

Longitudinal studies often collect only aggregate data, which allows only inefficient transition probability estimates. Barring enormous aggregate samples, improving the efficiency of transition probability estimates seems to be impossible without additional partial-transition data. This paper discusses several sampling plans that collect data of both types, as well as a methodology that combines them into efficient estimates of transition probabilities. The method handles both fixed and time-dependent categorical covariates and requires no assumptions (e.g., time homogeneity, Markov) about the population evolution.