Accounting for measurement error in biomarker data and misclassification of subtypes in the analysis of tumor data.

ABSTRACT: A common paradigm in dealing with heterogeneity across tumors in cancer analysis is to cluster the tumors into subtypes using marker data on the tumor, and then to analyze each of the clusters separately. A more specific target is to investigate the association between risk factors and specific subtypes and to use the results for personalized preventive treatment. This task is usually carried out in two steps-clustering and risk factor assessment. However, two sources of measurement error arise in these problems. The first is the measurement error in the biomarker values. The second is the misclassification error when assigning observations to clusters. We consider the case with a specified set of relevant markers and propose a unified single-likelihood approach for normally distributed bi

SUBMITTER: Nevo D 

PROVIDER: S-EPMC5562152 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature