A Bayesian model for estimating multi-state disease progression

Autor: William Hsu, Robert E. Weiss, Alex A. T. Bui, Simon X. Han, Panayiotis Petousis, Frank Meng, Shiwen Shen
Rok vydání: 2017
Předmět:
Chest x-ray
Male
Lung Neoplasms
Bayesian probability
Bayesian analysis
Health Informatics
Markov model
Bayesian inference
Sensitivity and Specificity
Severity of Illness Index
01 natural sciences
Article
010104 statistics & probability
03 medical and health sciences
symbols.namesake
0302 clinical medicine
Cancer screening
Covariate
Statistics
Econometrics
Humans
Computer Simulation
0101 mathematics
Overdiagnosis
Posterior predictive p-value
Early Detection of Cancer
Aged
Mathematics
Mean sojourn time
Models
Statistical
Observation error
Reproducibility of Results
Bayes Theorem
Markov chain Monte Carlo
Middle Aged
Markov Chains
Computer Science Applications
Transition probability
030220 oncology & carcinogenesis
Disease Progression
symbols
Radiographic Image Interpretation
Computer-Assisted
Female
National Lung Screening Trial
Lung cancer
Algorithms
Zdroj: Computers in Biology and Medicine. 81:111-120
ISSN: 0010-4825
DOI: 10.1016/j.compbiomed.2016.12.011
Popis: 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.
Databáze: OpenAIRE
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