ArticleStatistics in medicine2016
Hierarchical likelihood inference on clustered competing risks data.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies.BMC medical research methodology · 2023Pooled it
- H-Likelihood Approach on the Joint Frailty Model for Clustered Bivariate Survival Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Bias reduction methods in the hierarchical likelihood approach for shared frailty model of clustered failure time data.Journal of applied statistics · 2026Article
- A general class of small area estimation using calibrated hierarchical likelihood approach with applications to COVID-19 data.Journal of applied statistics · 2023Article
- Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach.Biostatistics (Oxford, England) · 2022Article
- Competing risks model for clustered data based on the subdistribution hazards with spatial random effects.Journal of applied statistics · 2022Article
- A Spatial Survival Model in Presence of Competing Risks for Iranian Gastrointestinal Cancer PatientsAsian Pacific journal of cancer prevention : APJCP · 2018Article
- Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties.Statistical methods in medical research · 2016Article
- Penalized Variable Selection for Joint AFT Random-Effect Model With Clustered Competing-Risks Data.Pharmaceutical statisticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause-specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset.
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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.