A Bayesian nonlinear mixed-effects disease progression model.

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.

[1]  Dongfeng Wu,et al.  Efficacy of Dual Lung Cancer Screening by Chest X-Ray and SputumCytology Using Johns Hopkins Lung Project Data , 2012 .

[2]  Dongfeng Wu,et al.  Estimation of sensitivity depending on sojourn time and time spent in preclinical state , 2016, Statistical methods in medical research.

[3]  Dongfeng Wu,et al.  MLE and Bayesian Inference of Age‐Dependent Sensitivity and Transition Probability in Periodic Screening , 2005, Biometrics.

[4]  M. Melamed,et al.  The National Cancer Institute Cooperative Early Lung Cancer Detection Program: Results of the Initial Screen (Prevalence) , 2015 .

[5]  M. Melamed,et al.  The National Cancer Institute Cooperative Early Lung Cancer Detection Program. Results of the initial screen (prevalence). Early lung cancer detection: Introduction. , 1984, The American review of respiratory disease.

[6]  S. Klawansky,et al.  A growth rate distribution model for the age dependence of human cancer incidence: a proposed role for promotion in cancer of the lung and breast. , 1984, Journal of theoretical biology.

[7]  Marvin Zelen,et al.  On the theory of screening for chronic diseases , 1969 .

[8]  Rhodri Hayward,et al.  Screening , 2008, The Lancet.

[9]  S. Shapiro,et al.  Periodic screening for breast cancer: the HIP Randomized Controlled Trial. Health Insurance Plan. , 1997, Journal of the National Cancer Institute. Monographs.

[10]  Dongfeng Wu,et al.  Sojourn time and lead time projection in lung cancer screening. , 2011, Lung cancer.

[11]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[12]  When Sensitivity is a Function of Age and Time Spent in the Preclinical State in Periodic Cancer Screening , 2008 .