Rank Tests for Independence for Bivariate Censored Data

Abstract

The paper discusses statistics that can be used to test whether two failure times, say $X_1$ and $X_2$, are independent. The two variables are subject to right censoring so that what is observed is $Y_i = \min(X_i, Z_i)$ and $\delta_i = I(X_i = Y_i)$, where $(Z_1, Z_2)$ are censoring times independent of $(X_1, X_2)$. Statistics that generalize the Spearman rank correlation and the log-rank correlation are considered, as well as general linear rank statistics. The Chernoff-Savage approach is adopted to show that suitably standardized versions of these statistics are asymptotically normal under both fixed and converging alternatives.