title: Estimating complex multi-state misclassification rates for biopsy-measured liver fibrosis in patients with hepatitis C.
abstract: For both clinical and research purposes, biopsies are used to classify liver damage known as fibrosis on an ordinal multi-state scale ranging from no damage to cirrhosis. Misclassification can arise from reading error (misreading of a specimen) or sampling error (the specimen does not accurately represent the liver). Studies of biopsy accuracy have not attempted to synthesize these two sources of error or to estimate actual misclassification rates from either source. Using data from two studies of reading error and two of sampling error, we find surprisingly large possible misclassification rates, including a greater than 50% chance of misclassification for one intermediate stage of fibrosis. We find that some readers tend to misclassify consistently low or consistently high, and some specimens tend to be misclassified low while others tend to be misclassified high. Non-invasive measures of liver fibrosis have generally been evaluated by comparison to simultaneous biopsy results, but biopsy appears to be too unreliable to be considered a gold standard. Non-invasive measures may therefore be more useful than such comparisons suggest. Both stochastic uncertainty and uncertainty about our model assumptions appear to be substantial. Improved studies of biopsy accuracy would include large numbers of both readers and specimens, greater effort to reduce or eliminate reading error in studies of sampling error, and careful estimation of misclassification rates rather than less useful quantities such as kappa statistics.
authors.affiliation: University of California, San Francisco, USA. peter@biostat.ucsf.edu
authors.forename: Peter
authors.initials: P
authors.lastname: Bacchetti
authors.forename: Ross
authors.initials: R
authors.lastname: Boylan
mesh_terms: D001706:Biopsy / Q000592:standards*; D019698:Hepatitis C, Chronic / Q000473:pathology*; D006801:Humans; D008103:Liver Cirrhosis / Q000145:classification / Q000473:pathology*; D015233:Models, Statistical; D015983:Selection Bias