Abstract:
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Risk profiles for cancer outcomes often vary by the presence of different tumor markers or subtypes. Increasing the number of markers adds an additional layer of complexity as markers are often correlated, and as a result, leads to difficulty in assessing subtype-specific effects of particular risk factors without considering other markers. Scientifically, it's also of interest to identify which marker or combination of markers is most relevant for disease. Finally, with a larger number of markers, the likelihood of missing marker information also increases and raises the question as to how to properly address this issue. In this paper, we apply methodology originally introduced by Rosner et al. to compute adjusted hazard-ratios that account for multiple correlated markers while evaluating four candidate approaches for missing tumor subtype. We consider the complete case, missing indicator, inverse probability weighting and multiple imputation approaches for missing tumor markers. We evaluate these four approaches using simulation studies and apply each to a real study of breast cancer risk factors considering multiple subtypes.
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