A Bayesian model for estimating multi-state disease progression.
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.
['Shen|Shiwen|S|', 'Han|Simon X|SX|', 'Petousis|Panayiotis|P|', 'Weiss|Robert E|RE|', 'Meng|Frank|F|', 'Bui|Alex A T|AA|', 'Hsu|William|W|']
D000368:Aged D000465:Algorithms D001499:Bayes Theorem D003198:Computer Simulation D018450:Disease Progression D055088:Early Detection of Cancer D005260:Female D006801:Humans D008175:Lung Neoplasms D008297:Male D008390:Markov Chains D008875:Middle Aged D015233:Models, Statistical D011857:Radiographic Image Interpretation, Computer-Assisted D015203:Reproducibility of Results D012680:Sensitivity and Specificity D012720:Severity of Illness Index
Mean sojourn time Markov model Observation error Chest x-ray Posterior predictive p-value Bayesian analysis Lung cancer Markov chain Monte Carlo Transition probability