title: 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 biomarkers. As an alternative, we consider a two-step procedure with the tumor type misclassification error taken into account in the second-step risk factor analysis. We describe our method for binary data and also for survival analysis data using a modified version of the Cox model. We present asymptotic theory for the proposed estimators. Simulation results indicate that our methods significantly lower the bias with a small price being paid in terms of variance. We present an analysis of breast cancer data from the Nurses' Health Study to demonstrate the utility of our method. Copyright © 2016 John Wiley & Sons, Ltd.
authors: Department of Statistics, Hebrew University, Jerusalem, Israel.|Departments of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.
authors: Daniel
authors: D
authors: Nevo
authors: Department of Statistics, Hebrew University, Jerusalem, Israel.
authors: David M
authors: DM
authors: Zucker
authors: Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.|Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, U.S.A.
authors: Rulla M
authors: RM
authors: Tamimi
authors: Departments of Biostatistics and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.
authors: Molin
authors: M
authors: Wang
mesh_terms: D015982:Bias; D015415:Biomarkers; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D009369:Neoplasms / Q000175:diagnosis; D016019:Survival Analysis
chemical_list: D015415:Biomarkers
keywords: classification; clustering; heterogeneity; measurement error; risk-factor analysis