Oncology Research Today is a free monthly online journal that collates and summarizes the latest research about Oncology, including details on cancer, surgery, chemotherapy, radiotherapy.

Tests of association under misclassification: application to histological sampling in oncology.

Betensky RA, Louis DN, Cairncross JG

Department of Biostatistics, Harvard School of Public Health, 655 Huntington Ave, Boston, MA 02115, USA. betensky@hsph.harvard.edu

Subjects in tumour studies are often misclassified with respect to histologic features that are not routinely recorded in diagnostic reports and that display heterogeneity within tumours. Pathologic analysis of the tumours may miss the feature of interest if the pathologist was not alerted to detail the microscopic feature of interest or if it is not present in the selected specimens. In this setting, only the subjects for whom the outcome is not found are potentially misclassified. Analyses of associations between the observed, potentially misclassified, outcome and a second outcome are invalid if the probability of misclassification depends on the second outcome. Three natural tests of association based on the observed data depend on different numbers of nuisance parameters. Most promising is a test based on the ratio of proportions of the observed feature. We illustrate this test using a study of the association of imaging parameters with genetic features in subjects with oligodendroglioma, a common brain tumour. In this study, calcification, a feature related to the imaging parameters, was potentially misclassified as not present.

Published 8 October 2007 in Stat Med, 26(26): 4808-16.
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