title: A Bayesian model for censored positive count data in evaluating breast cancer progression.
abstract: Basic science researchers transplant human cancer tissues from patients with ductal carcinomain situ(DCIS) to animals and observe the progression of the disease. Successful transplants show invasion of human tissues across mammary ducts in animal fat pads and cause DCIS-like lesions in one or more ducts. In this work, we consider data from a recent publication of breast cancer research where positive counts of affected ducts may be subject to censoring. We fit the data with zero-truncated Poisson (ZTP) models with an informative prior of gamma. Due to the zero-truncation and right censoring, posterior distributions may not be conventional gamma and are estimated through Markov chain Monte Carlo and grid approximation. For each of the two cell lines, we fit a model with group-specific parameters for DCIS subtypes classified by the cell surface biomarkers, and another model with a homogeneous parameter across groups. Models are compared by the Deviance Information Criterion (DIC). For the chosen prior parameter values, Bayes estimates are comparative to the maximum likelihood estimates, and the DIC favors the simpler model in both cell lines.
authors.affiliation: Department of Biostatistics, The University of Kansas Medical Center, Kansas City, Kansas 66160 ; The University of Kansas Cancer Center, Kansas City, Kansas 66160.
authors.forename: Hung-Wen
authors.initials: HW
authors.lastname: Yeh
authors.forename: Yu
authors.initials: Y
authors.lastname: Jiang
authors.forename: Lili
authors.initials: L
authors.lastname: Garrard
authors.forename: Yang
authors.initials: Y
authors.lastname: Lei
authors.forename: Byron
authors.initials: B
authors.lastname: Gajewski
keywords: Deviance Information Criterion; Markov Chain Monte Carlo; count data; grid approximation; right censoring; zero-truncated Poisson