IPCW Estimator for Kendall's Tau under Bivariate Censoring

Lajmi Lakhal 1 , Louis-Paul Rivest 2 ,  and David Beaudoin 3
  • 1 Université Laval
  • 2 Université Laval
  • 3 Université Laval

We investigate the nonparametric estimation of Kendall's coefficient of concordance, ?, for measuring the association between two variables under bivariate censoring. The proposed estimator is a modification of the estimator introduced by Oakes (1982), using a Horvitz-Thompson-type correction for the pairs that are not orderable. With censored data, a pair is orderable if one can establish whether the uncensored pair is discordant or concordant using the data available for that pair. Our estimator is shown to be consistent and asymptotically normally distributed. A simulation study shows that the proposed estimator performs well when compared with competing alternatives. The various methods are illustrated with a real data set.

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IJB publishes biostatistical models and methods, statistical theory, as well as original applications of statistical methods, for important practical problems arising from various sciences. It covers the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework, including advances in biostatistical computing.