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Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties.

Identifieur interne : 000148 ( Main/Exploration ); précédent : 000147; suivant : 000149

Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties.

Auteurs : Il Do Ha [Corée du Sud] ; Nicholas J. Christian [États-Unis] ; Jong-Hyeon Jeong [États-Unis] ; Junwoo Park [Corée du Sud] ; Youngjo Lee [Corée du Sud]

Source :

Statistical methods in medical research [ 1477-0334 ] ; 2016.

RBID : pubmed:24619110

Abstract

Competing risks data often exist within a center in multi-center randomized clinical trials where the treatment effects or baseline risks may vary among centers. In this paper, we propose a subdistribution hazard regression model with multivariate frailty to investigate heterogeneity in treatment effects among centers from multi-center clinical trials. For inference, we develop a hierarchical likelihood (or h-likelihood) method, which obviates the need for an intractable integration over the frailty terms. We show that the profile likelihood function derived from the h-likelihood is identical to the partial likelihood, and hence it can be extended to the weighted partial likelihood for the subdistribution hazard frailty models. The proposed method is illustrated with a dataset from a multi-center clinical trial on breast cancer as well as with a simulation study. We also demonstrate how to present heterogeneity in treatment effects among centers by using a confidence interval for the frailty for each individual center and how to perform a statistical test for such heterogeneity using a restricted h-likelihood.

DOI: 10.1177/0962280214526193
PubMed: 24619110

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en">Competing risks data often exist within a center in multi-center randomized clinical trials where the treatment effects or baseline risks may vary among centers. In this paper, we propose a subdistribution hazard regression model with multivariate frailty to investigate heterogeneity in treatment effects among centers from multi-center clinical trials. For inference, we develop a hierarchical likelihood (or h-likelihood) method, which obviates the need for an intractable integration over the frailty terms. We show that the profile likelihood function derived from the h-likelihood is identical to the partial likelihood, and hence it can be extended to the weighted partial likelihood for the subdistribution hazard frailty models. The proposed method is illustrated with a dataset from a multi-center clinical trial on breast cancer as well as with a simulation study. We also demonstrate how to present heterogeneity in treatment effects among centers by using a confidence interval for the frailty for each individual center and how to perform a statistical test for such heterogeneity using a restricted h-likelihood.</div>
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Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties.

Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties.

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Abstract

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