A
nonlinear mixed-effects approach is developed for disease progression models
that incorporate variation in age in a Bayesian framework. We further
generalize the probability model for sensitivity to depend on age at diagnosis,
time spent in the preclinical state and sojourn time. The developed models are
then applied to the Johns Hopkins Lung Project data and the Health Insurance
Plan for Greater New York data using Bayesian Markov chain Monte Carlo and are
compared with the estimation method that does not consider random-effects from
age. Using the developed models, we obtain not only age-specific
individual-level distributions, but also population-level distributions of
sensitivity, sojourn time and transition probability.
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