Inference on cancer screening exam accuracy using population-level administrative data.
This paper develops a model for cancer screening and cancer incidence data, accommodating the partially unobserved disease status, clustered data structures, general covariate effects, and dependence between exams. The true unobserved cancer and detection status of screening participants are treated as latent variables, and a Markov Chain Monte Carlo algorithm is used to estimate the Bayesian posterior distributions of the diagnostic error rates and disease prevalence. We show how the Bayesian approach can be used to draw inferences about screening exam properties and disease prevalence while allowing for the possibility of conditional dependence between two exams. The techniques are applied to the estimation of the diagnostic accuracy of mammography and clinical breast examination using data from the Ontario Breast Screening Program in Canada.
Analytics Informatics, Cancer Care Ontario, Toronto, ON, Canada.
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H
Jiang
Analytics Informatics, Cancer Care Ontario, Toronto, ON, Canada.|Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada.|Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
P E
PE
Brown
Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada.
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SD
Walter
D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics; D001943:Breast Neoplasms / Q000175:diagnosis; D003198:Computer Simulation; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008327:Mammography; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016017:Odds Ratio; D009864:Ontario; D011237:Predictive Value of Tests; D011336:Probability
Bayesian inference; cancer screening; clustered analysis; latent-class model; random effect; test accuracy