Generalized pairwise comparisons for censored data: An overview

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...

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Bibliographic Details
Published in:Biometrical journal 2023-02, Vol.65 (2), p.e2100354-n/a
Main Authors: Deltuvaite‐Thomas, Vaiva, Verbeeck, Johan, Burzykowski, Tomasz, Buyse, Marc, Tournigand, Christophe, Molenberghs, Geert, Thas, Olivier
Format: Article
Language:English
Subjects:
bias
censored outcome
Computer Simulation
generalized pairwise comparisons
Hypothesis testing
net benefit
Population
Probability
Research Design
Samples
Statistical analysis
Statistical power
Survival Analysis
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Summary: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, Péron, 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%.
ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.202100354