Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/38686
Title: Generalized pairwise comparisons for censored data: An overview
Authors: DELTUVAITE-THOMAS, Vaiva 
VERBEECK, Johan 
BURZYKOWSKI, Tomasz 
BUYSE, Marc 
Tournigand, Christophe
MOLENBERGHS, Geert 
THAS, Olivier 
Issue Date: 2023
Publisher: WILEY
Source: BIOMETRICAL JOURNAL, 65 (2) (Art N° 2100354)
Abstract: The method of generalized pairwise comparisons (GPC) is an extension of the well-known nonparametric Wilcoxon-Mann-Whitney test for comparing two groups of observations. Multiple generalizations of Wilcoxon-Mann-Whitney test and other GPC methods have been proposed over the years to handle censored data. These methods apply different approaches to handling loss of information due to censoring: ignoring noninformative pairwise comparisons due to censoring (Gehan, Harrell, and Buyse); imputation using estimates of the survival distribution (Efron, Peron, and Latta); or inverse probability of censoring weighting (IPCW, Datta and Dong). Based on the GPC statistic, a measure of treatment effect, the "net benefit," can be defined. It quantifies the difference between the probabilities that a randomly selected individual from one group is doing better than an individual from the other group. This paper aims at evaluating GPC methods for censored data, both in the context of hypothesis testing and estimation, and providing recommendations related to their choice in various situations. The methods that ignore uninformative pairs have comparable power to more complex and computationally demanding methods in situations of low censoring, and are slightly superior for high proportions (>40%) of censoring. If one is interested in estimation of the net benefit, Harrell's c index is an unbiased estimator if the proportional hazards assumption holds. Otherwise, the imputation (Efron or Peron) or IPCW (Datta, Dong) methods provide unbiased estimators in case of proportions of drop-out censoring up to 60%.
Notes: Deltuvaite-Thomas, V (corresponding author), Int Drug Dev Inst IDDI, 30 Av Prov, B-1341 Louvain La Neuve, Belgium.
vaiva.thomas@iddi.com
Keywords: bias;censored outcome;generalized pairwise comparisons;net benefit;statistical power
Document URI: http://hdl.handle.net/1942/38686
ISSN: 0323-3847
e-ISSN: 1521-4036
DOI: 10.1002/bimj.202100354
ISI #: 000855701200001
Rights: 2022 Wiley-VCH GmbH. This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available in the Supporting Information section. This article has earned an open data badge “Reproducible Research” for making publicly available the code necessary to reproduce the reported results. The results reported in this article were reproduced partially due to their computational complexity.
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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