title: Using semi-Markov processes to study timeliness and tests used in the diagnostic evaluation of suspected breast cancer.
abstract: Diagnostic evaluation of suspected breast cancer due to abnormal screening mammography results is common, creates anxiety for women and is costly for the healthcare system. Timely evaluation with minimal use of additional diagnostic testing is key to minimizing anxiety and cost. In this paper, we propose a Bayesian semi-Markov model that allows for flexible, semi-parametric specification of the sojourn time distributions and apply our model to an investigation of the process of diagnostic evaluation with mammography, ultrasound and biopsy following an abnormal screening mammogram. We also investigate risk factors associated with the sojourn time between diagnostic tests. By utilizing semi-Markov processes, we expand on prior work that described the timing of the first test received by providing additional information such as the mean time to resolution and proportion of women with unresolved mammograms after 90 days for women requiring different sequences of tests in order to reach a definitive diagnosis. Overall, we found that older women were more likely to have unresolved positive mammograms after 90 days. Differences in the timing of imaging evaluation and biopsy were generally on the order of days and thus did not represent clinically important differences in diagnostic delay. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, U.S.A.. rhubb@upenn.edu.
authors.forename: R A
authors.initials: RA
authors.lastname: Hubbard
authors.affiliation: Fred Hutchinson Cancer Research Center, Seattle, WA, U.S.A.
authors.forename: J
authors.initials: J
authors.lastname: Lange
authors.affiliation: Department of Internal Medicine, University of Utah, Salt Lake City, UT, U.S.A.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, U.S.A.
authors.forename: B A
authors.initials: BA
authors.lastname: Salim
authors.affiliation: McDonough School of Business, Georgetown University, Washington, DC, U.S.A.
authors.forename: J R
authors.initials: JR
authors.lastname: Stroud
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, U.S.A.
authors.forename: L Y T
authors.initials: LY
authors.lastname: Inoue
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D057210:Delayed Diagnosis; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D008327:Mammography; D008390:Markov Chains; D008403:Mass Screening
keywords: cancer; mammography; multistate model; semi-Markov model