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Biostatistics 4:167-178 (2003)
© 2003 Oxford University Press

Analysis of a molecular genetic neuro-oncology study with partially biased selection

Rebecca A. Betensky{dagger}, David N. Louis and J. Gregory Cairncross

Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA betensky{at}hsph.harvard.edu
Department of Pathology and Neurosurgical Service, Massachusetts General Hospital and Harvard Medical School, Boston MA, USA
Department of Oncology and Clinical Neurological Sciences, University of Western Ontario and London Regional Cancer Centre, London, Ontario, Canada

{dagger}To whom correspondence should be addressed

Oligodendrogliomas are a common variant of malignant brain tumors, and are unique for their relative sensitivity to chemotherapy and better prognosis. For these reasons, the identification of an objective oligodendroglial marker has been a long sought-after goal in the field of neuro-oncology. To this end, 75 patients who received chemotherapy at the London Regional Cancer Centre between 1984 and 1999 were studied (Ino et al., Clinical Cancer Research, 7, 839–845, 2001). Of these 75 patients, 50 were initially treated with chemotherapy (the current practice) and comprise a population-based sample. The remaining 25 patients were initially treated with radiation and were included in the study only because their tumor recurred, at which time they received chemotherapy. Because this group of 25 patients included neither those radiation patients whose tumors never recurred nor those radiation patients whose tumors recurred but were not treated with chemotherapy, issues of selection bias were of concern. For this reason, the initial analysis of these data included only the 50 population-based patients. This was unsatisfying given the rarity of this disease and of genetic information on this disease and led us to question whether we could undertake an analysis that includes all of the patients.

Here we examine approaches for utilizing the entire study population, as well as the assumptions required for doing so. We illustrate that there are both costs and benefits to using the 25 selected patients.

Keywords: Missing covariate; Omitted covariate; Selection bias


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