Identification of a competing risks model with unknown transformations of latent failure times

Lee, Sokbae

doi:10.1093/biomet/93.4.996

Cited by 23 publications

(18 citation statements)

References 24 publications

(3 reference statements)

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“…The dependence of our result on covariates is analogous to identifiability results in the competing risks literature. A number of authors have put forward identifiable competing risks models; and all of the results depend on the presence of covariates in the models (Heckman and Honore 1989; Abbring and van den Berg 2003; Lee 2006). …”

Section: Estimating Equations and Theoretical Resultsmentioning

confidence: 99%

A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

Othus

Tiwari

2009

Journal of the American Statistical Association

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Modern cancer treatments have substantially improved cure rates and have generated a great interest in and need for proper statistical tools to analyze survival data with non-negligible cure fractions. Data with cure fractions are often complicated by dependent censoring, and the analysis of this type of data typically involves untestable parametric assumptions on the dependence of the censoring mechanism and the true survival times. Motivated by the analysis of prostate cancer survival trends, we propose a class of semiparametric transformation cure models that allows for dependent censoring without making parametric assumptions on the dependence relationship. The proposed class of models encompasses a number of common models for the latency survival function, including the proportional hazards model and the proportional odds model, and also allows for time-dependent covariates. An inverse censoring probability reweighting scheme is used to derive unbiased estimating equations. We validate small sample properties with simulations and demonstrate the method with a data application.

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Section: Estimating Equations and Theoretical Resultsmentioning

confidence: 99%

A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

Othus

Tiwari

2009

Journal of the American Statistical Association

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“…The identification result of Lee (2006) also does not depend on either identification near zero or exclusion assumption. Lee shows that a parametrically specified g(x) can be identified up to scale and location normalization for a class of transformation models that include accelerated failure time competing risks models as special cases.…”

Section: Introductionmentioning

confidence: 82%

“…Abbring and Van den Berg (2003) provide weaker conditions than those assumed in Heckman and Honoré for the mixed proportional hazards competing risks model. Lee (2006) develops an identification result for a competing risks transformation model. Buera (2006) develops non-parametric identification and testable implications of the Roy model using assumptions that are distinct from ours, such as the continuity of regressors.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Identification of Accelerated Failure Time Competing Risks Models

Lee

Lewbel

2013

Econom. Theory

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We provide new conditions for identification of accelerated failure time competing risks models. These include Roy models and some auction models. In our setup, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identified given covariates that are independent of latent errors, provided that a certain rank condition is satisfied. We present a simple example in which our rank condition for identification is verified. Our identification strategy does not depend on identification at infinity or near zero, and it does not require exclusion assumptions. Given our identification, we show estimation can be accomplished using sieves.

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“…Interested readers are referred to Heckman and Taber () and Crowder () for comprehensive reviews. Extra difficulties arise because the conditional survival copula function changes with x , in contrast with the existing literature that assumes an invariant copula, such as in Heckman and Honoré (), Abbring and van den Berg (), and Lee and Lewbel (). The conditional survival copula reveals that the current model does not fit into the framework in Fan and Liu (), since the conditional survivor copula is neither Archimedean, nor does it admit a copula density function for dependent Lévy subordinators.…”

Section: Identification and Estimation With Competing Risksmentioning

confidence: 99%

A competing risks model with time‐varying heterogeneity and simultaneous failure

Liu¹

2020

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This paper proposes a new bivariate competing risks model in which both durations are the first passage times of dependent Lévy subordinators with exponential thresholds and multiplicative covariates effects. Our specification extends the mixed proportional hazards model, as it allows for the time-varying heterogeneity represented by the unobservable Lévy processes and it generates the simultaneous termination of both durations with positive probability. We obtain nonparametric identification of all model primitives given competing risks data. A flexible semiparametric estimation procedure is provided and illustrated through the analysis of a real dataset.

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Identification of a competing risks model with unknown transformations of latent failure times

Cited by 23 publications

References 24 publications

A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

Nonparametric Identification of Accelerated Failure Time Competing Risks Models

A competing risks model with time‐varying heterogeneity and simultaneous failure

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