A Bayesian Model for Estimating Multi-State Disease Progression

• Published in: Computers in biology and medicine 2017-02, Vol.81, p.111-120
• Main Authors: Shen, Shiwen, Han, Simon X, Petousis, Panayiotis, Weiss, Robert E, Meng, Frank, Bui, Alex A.T, Hsu, Willliam
• Format: Article
• Language: English
• Subjects: Age; Aged; Algorithms; Bayes Theorem; Bayesian analysis; Breast cancer; Cardiovascular disease; Chest x-ray; Clinical medicine; Computer Simulation; Diabetic retinopathy; Disease Progression; Early Detection of Cancer - methods; Female; Humans; Internal Medicine; Lung cancer; Lung Neoplasms - diagnostic imaging; Male; Markov chain Monte Carlo; Markov Chains; Markov model; Mean sojourn time; Medical imaging; Medical screening; Middle Aged; Models, Statistical; Observation error; Other; Posterior predictive p-value; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity; Severity of Illness Index; Transition probability
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2016.12.011

Abstract A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE.