Determining transition probabilities from mortality rates and autopsy findings

The Markov process is a useful tool for modeling the natural history of disease, which is becoming increasingly important as new diagnostic tests increase the detectability of early-stage disease. The accuracy of a Markov model, however, depends on the accuracy of the estimates for the transition probabilities between different stages of disease. Because these estimates are usually based on ''expert opinion'' or small cohort studies, they are subject to imprecision and bias. The authors describe an alternative method of estimating transition probabilities from the stage distribution of disease observed at the time of death and age-specific mortality rates from other causes. In addition, they prove that the transition probabilities are unique given certain assumptions about how they change with age. Finally, they illustrate the method using population-based data for prostate cancer.