ArticleStatistical methods in medical research2016
Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties.
Article in Statistical methods in medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- A hierarchical competing risks survival model in the study of child mortality in Bangladesh.Scientific reports · 2025Article
- Observational
- Competing risks model for clustered data based on the subdistribution hazards with spatial random effects.Journal of applied statistics · 2022Article
- Pretransplant survival of patients with end-stage heart failure under competing risks.PloS one · 2022Article
- An empirical comparison of time-to-event models to analyse a composite outcome in the presence of death as a competing risk.Contemporary clinical trials communications · 2020Article
- Evaluating center performance in the competing risks setting: Application to outcomes of wait-listed end-stage renal disease patients.Biometrics · 2018Article
- Hierarchical likelihood inference on clustered competing risks data.Statistics in medicine · 2016Article
- Variable selection in subdistribution hazard frailty models with competing risks data.Statistics in medicine · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.