On the identifiability of copulas in bivariate competing risks models

Schwarz, Maik; Jongbloed, Geurt; Keilegom, Ingrid Van

doi:10.1002/cjs.11179

Cited by 17 publications

(5 citation statements)

References 66 publications

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“…[46][47][48][49] Indeed, for the copula to be identifiable from data, one needs to impose proportional hazards models, 50 parametric marginal survival models, 25,26,29 or other restrictions. 51 If no parametric assumption is made on marginal survival functions, the copula needs to be assumed. 43,52 This is the case for the proposed method.…”

Section: Sensitivity Analysismentioning

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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Section: Sensitivity Analysismentioning

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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“…More recent results on identifiability can be found for example in Escarela and Carriere [3] considering Frank copula and parametric models. Schwarz et al in [12] deal with non-parametric setting and study the inverse problem how from marginal distributions the joint distribution can be estimated, under a given copula class assumption.…”

Section: Competing Risks and Non-identifiability Problemmentioning

confidence: 99%

Statistical analysis and application of competing risks model with regression

Volf

2019

Cro. Oper. Res. Rev.

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The paper deals with the methods of statistical analysis of dependent competing risks in the presence of covariates. The problem of identification of marginal and joint distributions of competing random variables is recalled and certain identifiability results in the framework of regression models are presented. The main objective is then to study the case when the correlation of competing variables depends on covariates, as this phenomenon has not been taken into account in the most of papers dealing with the identifiability of competing risks models with regression. Such a dependence is demonstrated and estimated on a real example with unemployment data.

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“…This approach is appealing as the distribution of other risks is often unknown in applications. Schwarz et al (2013) show identifiability of the dependence parameter θ when R(c) is unknown, although their model requires a rather restrictive independence assumption on observed duration X and the risk indicator δ. This assumption is violated when, for example, the two risks have different cumulative incidence functions.…”

Section: Introductionmentioning

confidence: 99%

A single risk approach to the semiparametric copula competing risks model

Lo¹,

Wilke²

2022

Preprint

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A typical situation in competing risks analysis is that the researcher is only interested in a subset of risks. This paper considers a depending competing risks model with the distribution of one risk being a parametric or semi-parametric model, while the model for the other risks being unknown. Identifiability is shown for popular classes of parametric models and the semiparametric proportional hazards model. The identifiability of the parametric models does not require a covariate, while the semiparametric model requires at least one. Estimation approaches are suggested which are shown to be √ n-consistent. Applicability and attractive finite sample performance are demonstrated with the help of simulations and data examples.

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On the identifiability of copulas in bivariate competing risks models

Cited by 17 publications

References 66 publications

Factorial survival analysis for treatment effects under dependent censoring

Factorial survival analysis for treatment effects under dependent censoring

Statistical analysis and application of competing risks model with regression

A single risk approach to the semiparametric copula competing risks model

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