Nonparametric Inference for Clustered Competing Risks Data

• Activity Number: 503
• Type: Contributed
• Date/Time: Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Bingshu Chen*+ and Philip S. Rosenberg and Joan L. Kramer and Mark H. Greene
• Companies: National Institutes of Health and National Institutes of Health and National Institutes of Health and National Institutes of Health
• Keywords: Competing risks ; Correlated survival data ; Counting processes ; Nonparametric estimate ; Two-sample test

Abstract

Clustering arises naturally in clinical genetic studies and other settings. We develop a nonparametric estimator of cumulative incidence, and obtain robust pointwise standard errors, that account for within-cluster correlation. We modify the two-sample Gray and Pepe-Mori tests for correlated competing risks data, and propose a simple two-sample test of the difference in cumulative incidence at a landmark time. In simulation studies, our estimators are asymptotically unbiased, and the modified test statistics control the type I error. We apply our methods to a family-based prospective cohort study of hereditary breast/ovarian cancer families. For women with BRCA1 mutations, we estimate the cumulative incidence of breast cancer in the presence of competing mortality from ovarian cancer, accounting for significant within-family correlation.