Estimation of the Mann–Whitney effect in the two-sample problem under dependent censoring

Takeshi Emura*, Jiun Huang Hsu

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

13 Scopus citations

Abstract

The Mann–Whitney effect is a nonparametric measure for comparing the distribution between two groups, which can be estimated by right-censored data. However, the traditional estimator of the Mann–Whitney effect based on the Kaplan–Meier estimators can be inconsistent when the independent censoring assumption fails to hold. Investigation is made on the asymptotic bias of the traditional estimator of the Mann–Whitney effect when the independent censoring assumption is violated due to dependence between survival time and censoring time. A new estimator of the Mann–Whitney effect is proposed by applying the copula-graphic estimator to adjust for the effect of dependent censoring. The proposed estimator and test are consistent when the assumed copulas for the two groups are correct. Some consistency properties under misspecified copulas are also given. Simulations are conducted to verify the proposed method under possible misspecification on copulas. The method is illustrated by a real data set. We provide an R function “MW.test” to implement the proposed estimator and test.

Original languageEnglish
Article number106990
JournalComputational Statistics and Data Analysis
Volume150
DOIs
StatePublished - 10 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

  • Copula
  • Copula-graphic estimator
  • Dependent censoring
  • Log-rank test
  • Mann–Whitney test
  • Two-sample problem

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