Competing Risks Analysis of Correlated Failure Time Data
作者： Bingshu E. Chen , Joan L. Kramer , Mark H. Greene , Philip S. Rosenberg
DOI: 10.1111/J.1541-0420.2007.00868.X
关键词: Alternative hypothesis 、 Econometrics 、 Type I and type II errors 、 Medicine 、 Statistical hypothesis testing 、 Estimator 、 Correlation 、 Statistics 、 Cumulative incidence 、 Breast cancer 、 Standard error 、 General Biochemistry, Genetics and Molecular Biology 、 Statistics and Probability 、 General Immunology and Microbiology 、 Applied mathematics 、 General Agricultural and Biological Sciences 、 General Medicine
摘要: 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. a nonparametric estimator of cumulative incidence, obtain robust pointwise standard errors that account within-cluster correlation. modify the two-sample Gray Pepe-Mori tests data, propose simple test difference incidence at landmark time. In simulation studies, our estimators asymptotically unbiased, modified statistics control type I error. The power respective is differentially sensitive to degree correlation; optimal depends on alternative hypothesis interest For purposes illustration, we apply family-based prospective cohort study hereditary breast/ovarian cancer families. women with BRCA1 mutations, estimate breast presence mortality from ovarian cancer, accounting significant within-family
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