Bayesian Estimation of the Three Key Parameters in CT for the National Lung Screening Trial DataRuiqi Liu1, Beth Levitt2, Tom Riley2 and Dongfeng Wu1*
- *Corresponding Author:
- Dongfeng Wu
Department of Bioinformatics andBiostatistics
University of Louisville, Louisville
E-mail: [email protected]
Received date: November 19, 2015; Accepted date: December 01, 2015; Published date:December 08, 2015
Citation: Liu R, Levitt B, Riley T, Wu D (2015) Bayesian Estimation of the Three Key Parameters in CT for the National Lung Screening Trial Data. J Biom Biostat 6:263. doi:10.4172/2155-6180.1000263
Copyright: © 2015 Liu R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In this study cancer screening likelihood method was used to analyze the CT scan group in the National Lung Screening Trial (NLST) data. Three key parameters: screening sensitivity, transition probability density from disease free to preclinical state, and sojourn time in the preclinical state, were estimated using Bayesian approach and Markov Chain Monte Carlo simulations. The sensitivity for lung cancer screening using CT scan is high; it does not depend on a patient’s age, and is slightly higher in females than in males. The transition probability from the disease-free to the preclinical state has a peak around age 70 for both genders, which agrees with the fact that the highest lung cancer incidence rate appears between age 65 and 74. The posterior mean sojourn time is around 1.5 years for all groups, and that explains why screening only have a short time interval to catch lung cancer. Accurate estimation of the three key parameters is critical for other estimations such as lead time and over-diagnosis, because these quantities are functions of the three key parameters.