Premium
NON‐PARAMETRIC INFERENCE FOR CUMULATIVE INCIDENCE FUNCTIONS IN COMPETING RISKS STUDIES
Author(s) -
LIN D. Y.
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970430)16:8<901::aid-sim543>3.0.co;2-m
Subject(s) - mathematics , estimator , statistics , cumulative distribution function , resampling , cumulative incidence , confidence interval , covariance , parametric statistics , econometrics , probability density function , cohort
In the competing risks problem, a useful quantity is the cumulative incidence function, which is the probability of occurrence by time t for a particular type of failure in the presence of other risks. The estimator of this function as given by Kalbfleisch and Prentice is consistent, and, properly normalized, converges weakly to a zero‐mean Gaussian process with a covariance function for which a consistent estimator is provided. A resampling technique is developed to approximate the distribution of this process, which enables one to construct confidence bands for the cumulative incidence curve over the entire time span of interest and to perform Kolmogorov–Smirnov type tests for comparing two such curves. An AIDS example is provided. © 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 901–910 (1997).