The Identifiability of the Mixed Proportional Hazards Competing Risks Model

Abbring, Jaap H.; Berg, Gérard J. van den

doi:10.1111/1467-9868.00410

Cited by 151 publications

(141 citation statements)

References 21 publications

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“…Under mixed proportional hazard specification (Abbring and van den Berg 2003), the hazard rates of the failure times for two related individuals depend multiplicatively on the respective baseline hazards λ j (t), regressor functions χ j (u j ) with observed vector of covariates u j , and unobserved nonnegative random variable (frailty) Z j , characterizing the heterogeneity in the population with respect to hazard λ j…”

Section: Survival Analysis Under a Frailty Settingmentioning

confidence: 99%

“…Yashin and Iachine (1999) proved the identifiability of the correlated frailty model without observed covariates assuming that Z 1 and Z 2 are gamma distributed. Abbring and van den Berg (2003) studied the identifiability of the mixed proportional hazards competing risks model. We adopt this method to investigate the identifiability of the mixed bivariate survival model for time-dependent correlated frailties.…”

Section: Model Identifiabilitymentioning

confidence: 99%

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Study of the bivariate survival data using frailty models based on Lévy processes

Begun

Yashin

2018

AStA Adv Stat Anal

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Frailty models allow us to take into account the non-observable inhomogeneity of individual hazard functions. Although models with time-independent frailty have been intensively studied over the last decades and a wide range of applications in survival analysis have been found, the studies based on the models with time-dependent frailty are relatively rare. In this paper, we formulate and prove two propositions related to the identifiability of the bivariate survival models with frailty given by a nonnegative bivariate Lévy process. We discuss parametric and semiparametric procedures for estimating unknown parameters and baseline hazard functions. Numerical experiments with simulated and real data illustrate these procedures. The statements of the propositions can be easily extended to the multivariate case.

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Section: Survival Analysis Under a Frailty Settingmentioning

confidence: 99%

Section: Model Identifiabilitymentioning

confidence: 99%

Study of the bivariate survival data using frailty models based on Lévy processes

Begun

Yashin

2018

AStA Adv Stat Anal

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“…Abbring and Van den Berg show that these models are nonparametrically identified from singlespell data under the conditions for the identification of competing-risks models based on the multivariate MPH model given by Abbring and Van den Berg (2003a). Among other conditions are the requirements that there is some independent local variation of the regressor effects in both hazard rates and a finite-mean restriction on V , which are standard in the analysis of multivariate MPH models.…”

Section: Identifiability Without Exclusion Restrictionsmentioning

confidence: 99%

Dynamic policy analysis

Abbring

Heckman

2008

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractThis chapter studies the microeconometric treatment-effect and structural approaches to dynamic policy evaluation. First, we discuss a reduced-form approach based on a sequential randomization or dynamic matching assumption that is popular in biostatistics. We then discuss two complementary approaches for treatments that are single stopping times and that allow for non-trivial dynamic selection on unobservables. The first builds on continuous-time duration and event-history models. The second extends the discrete-time dynamic discrete-choice literature.

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“…Other authors have focused on identifying which mathematical constraints need to be imposed on multi-risk survival analysis models in order to circumvent the identifiability problem of [1], and infer the joint event-time distribution unambiguously from survival data e.g. [32,33,34].…”

Section: Introductionmentioning

confidence: 99%

A latent class model for competing risks

et al. 2017

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Survival data analysis becomes complex when the proportional hazards assumption is violated at population level, or when crude hazard rates are no longer estimators of marginal ones. We develop a Bayesian survival analysis method to deal with these situations, based on assuming that the complexities are induced by latent cohort or disease heterogeneity that is not captured by covariates, and that proportional hazards hold at the level of individuals. This leads to a description from which risk-specific marginal hazard rates and survival functions are fully accessible, 'decontaminated' of the effects of informative censoring, and which includes Cox, random effects and latent class models as special cases. Simulated data confirm that our approach can map a cohort's substructure, and remove heterogeneity-induced informative censoring effects. Application to data from the ULSAM cohort leads to plausible alternative explanations for previous counter-intuitive inferences on prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to data from the AMORIS study.

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The Identifiability of the Mixed Proportional Hazards Competing Risks Model

Cited by 151 publications

References 21 publications

Study of the bivariate survival data using frailty models based on Lévy processes

Study of the bivariate survival data using frailty models based on Lévy processes

Dynamic policy analysis

A latent class model for competing risks

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