Analysis of cohort studies with multivariate and partially observed disease classification data

• Published in: Biometrika 2010-09, Vol.97 (3), p.683-698
• Main Authors: Chatterjee, Nilanjan, Sinha, Samiran, Diver, W. Ryan, Feigelson, Heather Spencer
• Format: Article
• Language: English
• Subjects: Analytical estimating; Breast cancer; Cancer; Classification; Competing-risk; Data processing; Diabetes; Disease; Disease models; Epidemiology; Estimating techniques; Estimation methods; Estimators; Etiologic heterogeneity; Heart diseases; Influence function; Information management; Mathematical models; Missing cause of failure; Nutrition; Parametric models; Partial likelihood; Pathology; Proportional hazard regression; Regression analysis; Simulation; Studies; Two-stage model; Unbiased estimators
• ISSN: 0006-3444; 1464-3510; 1464-3510; 0006-3444
• DOI: 10.1093/biomet/asq036

Complex diseases like cancers can often be classified into subtypes using various pathological and molecular traits of the disease. In this article, we develop methods for analysis of disease incidence in cohort studies incorporating data on multiple disease traits using a two-stage semiparametric Cox proportional hazards regression model that allows one to examine the heterogeneity in the effect of the covariates by the levels of the different disease traits. For inference in the presence of missing disease traits, we propose a generalization of an estimating equation approach for handling missing cause of failure in competing-risk data. We prove asymptotic unbiasedness of the estimating equation method under a general missing-at-random assumption and propose a novel influence-function-based sandwich variance estimator. The methods are illustrated using simulation studies and a real data application involving the Cancer Prevention Study II nutrition cohort.