title: Estimation of sensitivity depending on sojourn time and time spent in preclinical state.
abstract: The probability model for periodic screening was extended to provide statistical inference for sensitivity depending on sojourn time, in which the sensitivity was modeled as a function of time spent in the preclinical state and the sojourn time. The likelihood function with the proposed sensitivity model was then evaluated with simulated data to check its reliability in terms of the mean estimation and the standard error. Simulation results showed that the maximum likelihood estimates of the proposed model have little bias and small standard errors. The extended probability model was further applied to the Johns Hopkins Lung Project data using both maximum likelihood estimation and Bayesian Markov chain Monte Carlo.
authors.affiliation: Biostatistics Core, Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA kimse@karmanos.org.
authors.forename: Seongho
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA dongfeng.wu@louisville.edu.
authors.forename: Dongfeng
authors.initials: D
authors.lastname: Wu
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009010:Monte Carlo Method; D011230:Precancerous Conditions; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors
keywords: Sensitivity; cancer screening; sojourn time; transition probability