title: A Bayesian model for estimating multi-state disease progression.
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.
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA, USA; Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA. Electronic address: shiwenshen@ucla.edu.
authors.forename: Shiwen
authors.initials: S
authors.lastname: Shen
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA, USA; Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
authors.forename: Simon X
authors.initials: SX
authors.lastname: Han
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA, USA; Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
authors.forename: Panayiotis
authors.initials: P
authors.lastname: Petousis
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, CA, USA.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Weiss
authors.affiliation: Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
authors.forename: Frank
authors.initials: F
authors.lastname: Meng
authors.affiliation: Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
authors.forename: Alex A T
authors.initials: AA
authors.lastname: Bui
authors.affiliation: Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
authors.forename: William
authors.initials: W
authors.lastname: Hsu
mesh_terms: D000368:Aged; D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D018450:Disease Progression; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index
keywords: Bayesian analysis; Chest x-ray; Lung cancer; Markov chain Monte Carlo; Markov model; Mean sojourn time; Observation error; Posterior predictive p-value; Transition probability