Distribution free tests for stochastic ordering in the competing risks model

Bagai, Isha; Deshpandé, Jayant V.; Kochar, Subhash C.

doi:10.1093/biomet/76.4.775

Cited by 20 publications

(4 citation statements)

References 5 publications

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“…Note that for c = 0 (no censoring), we obtain similar results to [3]. Hoeffding [6] (Theorem 7.1) showed that Theorem 3.2.…”

Section: Asymptotic Resultssupporting

confidence: 73%

“…Then, Yip and Lam [8] show that methods based on the independence assumption can be used while dealing with these pooled risks. The results of this Note complete and extend the results of Bagai et al [3]; we propose a generalization to take in account classical cohort studies with censored data.…”

Section: Introductionsupporting

confidence: 82%

“…Chiang [4] and Crowder [5] propose a complete review of the competing risks literature. Bagai et al [3] propose distribution-free procedures for testing the equality of two independent failure distributions. Locally, most powerful rank tests are derived, and a generalization of the Wilcoxon tests is also proposed.…”

Section: Introductionmentioning

confidence: 99%

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A U-statistic test in competing risk models

Molinari

2005

Comptes Rendus. Mathématique

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“…Note that for c = 0 (no censoring), we obtain similar results to [3]. Hoeffding [6] (Theorem 7.1) showed that Theorem 3.2.…”

Section: Asymptotic Resultssupporting

confidence: 73%

Section: Introductionsupporting

confidence: 82%

See 1 more Smart Citation

A U-statistic test in competing risk models

Molinari

2005

Comptes Rendus. Mathématique

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“…The above statistic is proposed in equation (2.6) by Bagai et al (1989) for testing the equality of failure rates of two independent competing risks.…”

Section: Test Based On U 1 (T) Considermentioning

confidence: 99%

On Testing Dependence between Time to Failure and Cause of Failure via Conditional Probabilities

Dewan¹,

Deshpandé²,

Kulathinal³

2004

Scandinavian J Statistics

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Dependence structures between the failure time and the cause of failure are expressed in terms of the monotonicity properties of the conditional probabilities involving the cause of failure and the failure time. These properties of the conditional probabilities are used for testing four types of departures from the independence of the failure time and the cause of failure and tests based on "U"-statistics are proposed. In the process, a concept of concordance and discordance between a continuous and a binary variable is introduced to propose a statistical test. The proposed tests are applied to two illustrative applications. Copyright Board of the Foundation of the Scandinavian Journal of Statistics 2004.

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Competing Risk Analysis

Gupta

2012

Encyclopedia of Environmetrics

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In studies of risk assessment due to environmental exposures, we are interested in the mortality due to a particular disease when several other diseases are acting on a person's life. The method of analyzing survival data in such a population is called “competing risk analysis.” We also have a fundamental assumption that each death takes place due to a single cause. When an individual is put on test, only his time of death and cause of death are recorded. This competing risk data provides insufficient information to determine the joint distribution of the survival times giving rise to an identifiability problem. In this article, the cause‐specific hazard functions are introduced and their estimation and testing, for modeling, both independent and dependent lifetimes are discussed. The identifiability problem is also addressed and some ways to deal with the competing risk data in the presence of association between the latent survival times are suggested.

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Distribution free tests for stochastic ordering in the competing risks model

Cited by 20 publications

References 5 publications

A U-statistic test in competing risk models

A U-statistic test in competing risk models

On Testing Dependence between Time to Failure and Cause of Failure via Conditional Probabilities

Competing Risk Analysis

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