Bayesian estimation of the probability of asbestos exposure from lung fiber counts.
Asbestos exposure is a well-known risk factor for various lung diseases, and when they occur, workmen's compensation boards need to make decisions concerning the probability the cause is work related. In the absence of a definitive work history, measures of short and long asbestos fibers as well as counts of asbestos bodies in the lung can be used as diagnostic tests for asbestos exposure. Typically, data from one or more lung samples are available to estimate the probability of asbestos exposure, often by comparing the values with those from a reference nonexposed population. As there is no gold standard measure, we explore a variety of latent class models that take into account the mixed discrete/continuous nature of the data, that each subject may provide data from more than one lung sample, and that the within-subject results across different samples may be correlated. Our methods can be useful to compensation boards in providing individual level probabilities of exposure based on available data, to researchers who are studying the test properties for the various measures used in this area, and more generally, to other test situations with similar data structure.
Weichenthal
Scott
S
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal, Quebec, H3A 1A2, Canada.
Joseph
Lawrence
L
Bélisle
Patrick
P
Dufresne
André
A
D001194:Asbestos / Q000009:adverse effects / Q000032:analysis*; D001499:Bayes Theorem; D006801:Humans; D008168:Lung / Q000473:pathology*; D008722:Methods; D016273:Occupational Exposure / Q000032:analysis* / Q000706:statistics & numerical data; D011336:Probability; D014942:Workers' Compensation
D001194:Asbestos