Competing risks analysis of correlated failure time data.
We develop methods for competing risks analysis when individual event times are correlated within clusters. 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. The power of the respective two-sample tests is differentially sensitive to the degree of correlation; the optimal test depends on the alternative hypothesis of interest and the within-cluster correlation. For purposes of illustration, 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.
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20852, USA. bingshu@chenstat.com
Bingshu E
BE
Chen
Joan L
JL
Kramer
Mark H
MH
Greene
Philip S
PS
Rosenberg
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000401:mortality*; D015897:Comorbidity; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D015994:Incidence; D008954:Models, Biological; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000401:mortality*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013223:Statistics as Topic; D016019:Survival Analysis; D015996:Survival Rate