title: Modeling grade progression in an active surveillance study.
abstract: Prostate cancer grade, assessed with the Gleason score, describes how abnormal the tumor tissue and cells appear, and it is an important prognostic indicator of disease progression. Whether prostate tumors change grade is a question that has implications for screening and treatment. Empirical data on tumor grade over time have become available from men biopsied regularly as part of active surveillance (AS). However, biopsy (BX) grade is subject to misclassification. In this article, we develop a model that allows for estimation of the time of grade change while accounting for the misclassification error from BX grade. We use misclassification rates from studies of prostate cancer BXs followed by radical prostatectomy. Estimation of the transition times from true low-grade to high-grade disease is conducted within a Bayesian framework. We apply our model to serial observations on BX grade among 627 cases enrolled in a cohort of AS patients at Johns Hopkins University who were biopsied annually and referred to treatment if there was any evidence of disease progression on BX. We consider different prior distributions for the time to true grade progression. The estimated likelihood of grade progression within 10 years of study entry ranges from 12% to 24% depending on the prior. We conclude that knowledge of rates of grade misclassification allows for determination of true grade progression rates among men with serial BXs on AS. Although our results are sensitive to prior specifications, they indicate that in a nontrivial fraction of the patient population, tumor grade can progress.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, U.S.A.
authors.forename: Lurdes Y T
authors.initials: LY
authors.lastname: Inoue
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: Trock
authors.forename: Alan W
authors.initials: AW
authors.lastname: Partin
authors.forename: Herbert B
authors.initials: HB
authors.lastname: Carter
authors.forename: Ruth
authors.initials: R
authors.lastname: Etzioni
mesh_terms: D001499:Bayes Theorem; D001706:Biopsy; D056808:Biostatistics; D054337:Cell Dedifferentiation; D015331:Cohort Studies; D003198:Computer Simulation; D018450:Disease Progression; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D060787:Neoplasm Grading / Q000706:statistics & numerical data*; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000601:surgery; D057832:Watchful Waiting
keywords: Bayesian analysis; prostate cancer; simulation modeling