Group sequential methods for the Mann-Whitney parameter

Nowak, Claus P.; Mütze, Tobias; Konietschke, Frank

doi:10.1177/09622802221107103

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…[11] investigated the effect of dependence of the valiables on p in the stress-strength model with the exponential margins. [12] proposed a group sequential method for estimating the Mann-Whitney parameter. [13] studied the estimator of p in point, interval, and Bayesian estimations when the stress valiables follow geometric and Lindley distribution.…”

Section: Introductionmentioning

confidence: 99%

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Nakazono,

Lin,

Liao

et al. 2024

Preprint

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The Mann-Whitney effect is a measure for comparing survival distributions between two groups. The Mann-Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann-Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent each other, the traditional expression of the Mann-Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann-Witney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands (https://nkosuke.shinyapps.io/shiny_survival/). Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

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Section: Introductionmentioning

confidence: 99%

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Nakazono,

Lin,

Liao

et al. 2024

Preprint

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“…Patil et al [12] investigated the effect of dependence of the variables on p in the stress-strength model with the exponential margins. Nowak et al [13] proposed a group sequential method for estimating the Mann-Whitney parameter. Singh et al [14] studied the estimator of p in point, interval, and Bayesian estimations when the stress variables follow geometric and Lindley distribution.…”

Section: Introductionmentioning

confidence: 99%

Computation of the Mann–Whitney Effect under Parametric Survival Copula Models

Nakazono,

Lin,

Liao

et al. 2024

Mathematics

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The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann–Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent of each other, the traditional formula of the Mann–Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann–Whitney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands . Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

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“…For d = 2, this pairwise effects of treatments have been useful in two-sample comparisons. 34–38 However, in factorial analyses involving multiple treatments, the pairwise effects have to be aggregated to characterize the relative treatment effects.…”

Section: Introductionmentioning

confidence: 99%

Factorial survival analysis for treatment effects under dependent censoring

Emura,

Ditzhaus,

Dobler

et al. 2023

Stat Methods Med Res

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Factorial analyses offer a powerful nonparametric means to detect main or interaction effects among multiple treatments. For survival outcomes, for example, from clinical trials, such techniques can be adopted for comparing reasonable quantifications of treatment effects. The key difficulty to solve in survival analysis concerns the proper handling of censoring. So far, all existing factorial analyses for survival data have been developed under the independent censoring assumption, which is too strong for many applications. As a solution, the central aim of this article is to develop new methods for factorial survival analyses under quite general dependent censoring regimes. This will be accomplished by combining existing nonparametric methods for factorial survival analyses with techniques developed for survival copula models. As a result, we will present an appealing F-test that exhibits sound performance in our simulation study. The new methods are illustrated in a real data analysis. We implement the proposed method in an R function surv.factorial(.) in the R package compound.Cox.

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Group sequential methods for the Mann-Whitney parameter

Cited by 3 publications

References 44 publications

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Computation of The Mann-Whitney Effect under Parametric Survival Copula Models

Computation of the Mann–Whitney Effect under Parametric Survival Copula Models

Factorial survival analysis for treatment effects under dependent censoring

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