ArticleBiometrics2011
Frailty-based competing risks model for multivariate survival data.
Article in Biometrics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- An Overview and Recent Developments in the Analysis of Multistate Processes.Statistics in medicine · 2026Review
- PERSONALIZED RISK PREDICTION FOR CANCER SURVIVORS: A GENERALIZED BAYESIAN SEMI-PARAMETRIC MODEL OF RECURRENT EVENTS WITH COMPETING OUTCOMES.The annals of applied statistics · 2025Article
- Bayesian treatment screening and selection using subgroup-specific utilities of response and toxicity.Biometrics · 2023Article
- A double-Cox model for non-proportional hazards survival analysis with frailty.Statistics in medicine · 2023Article
- Semiparametric regression analysis of bivariate censored events in a family study of Alzheimer's disease.Biostatistics (Oxford, England) · 2022Article
- Competing risks model for clustered data based on the subdistribution hazards with spatial random effects.Journal of applied statistics · 2022Article
- Precision Bayesian phase I-II dose-finding based on utilities tailored to prognostic subgroups.Statistics in medicine · 2021Article
- A competing risks model with binary time varying covariates for estimation of breast cancer risks inStatistical methods in medical research · 2021Article
- Association of Risk-Reducing Salpingo-Oophorectomy With Breast Cancer Risk in Women With BRCA1 and BRCA2 Pathogenic Variants.JAMA oncology · 2021Article
- A phase I-II design based on periodic and continuous monitoring of disease status and the times to toxicity and death.Statistics in medicine · 2020Article
- Risk-adjusted cUSUM control charts for shared frailty survival models with application to hip replacement outcomes: a study using the NJR dataset.BMC medical research methodology · 2019Article
- Parametric estimation of association in bivariate failure-time data subject to competing risks: sensitivity to underlying assumptions.Lifetime data analysis · 2019Review
- Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
- Investigating hospital heterogeneity with a competing risks frailty model.Statistics in medicine · 2019Article
- Bayesian Semiparametric Estimation of Cancer-specific Age-at-onset Penetrance with Application to Li-Fraumeni Syndrome.Journal of the American Statistical Association · 2019Article
- Evaluating center performance in the competing risks setting: Application to outcomes of wait-listed end-stage renal disease patients.Biometrics · 2018Article
- Second-Order Estimating Equations for Clustered Current Status Data from Family Studies Using Response-Dependent Sampling.Statistics in biosciences · 2018Article
- Estimating State Transitions for Opioid Use Disorders.Medical decision making : an international journal of the Society for Medical Decision Making · 2017Article
- A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data.Scandinavian journal of statistics, theory and applications · 2017Article
- Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties.Statistical methods in medical research · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In this work, we provide a new class of frailty-based competing risks models for clustered failure times data. This class is based on expanding the competing risks model of Prentice et al. (1978, Biometrics 34, 541-554) to incorporate frailty variates, with the use of cause-specific proportional hazards frailty models for all the causes. Parametric and nonparametric maximum likelihood estimators are proposed. The main advantages of the proposed class of models, in contrast to the existing models, are: (1) the inclusion of covariates; (2) the flexible structure of the dependency among the various types of failure times within a cluster; and (3) the unspecified within-subject dependency structure. The proposed estimation procedures produce the most efficient parametric and semiparametric estimators and are easy to implement. Simulation studies show that the proposed methods perform very well in practical situations.
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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.