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

Takeshi Emura and Jiun-Huang Hsu

Computational Statistics & Data Analysis, 2020, vol. 150, issue C

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.

Keywords: Mann–Whitney test; Copula; Copula-graphic estimator; Dependent censoring; Log-rank test; Two-sample problem (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:150:y:2020:i:c:s0167947320300815

DOI: 10.1016/j.csda.2020.106990

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