ArticleJournal of the American Statistical Association2016
Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example.Research synthesis methods · 2020Pooled it
- The Effect of Rural Versus Urban Residence on Risk of Unplanned Readmission and Death in Older Veterans Discharged From VA Hospitals.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026Observational
- A semicompeting risks model with an application to UK Biobank data to identify risk factors for diabetes onset and progression.Biometrics · 2025Article
- An augmented illness-death model for semi-competing risks with clinically immediate terminal events.Statistics in medicine · 2024Article
- Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Competing Risk Modeling with Bivariate Varying Coefficients to Understand the Dynamic Impact of COVID-19.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Predicting the Risk of Alzheimer's Disease and Related Dementia in Patients with Mild Cognitive Impairment Using a Semi-Competing Risk Approach.Informatics (MDPI) · 2023Article
- Analysis of hospital readmissions with competing risks.Statistical methods in medical research · 2022Article
- Article
- MEASURING PERFORMANCE FOR END-OF-LIFE CARE.The annals of applied statistics · 2022Article
- Competing risks model for clustered data based on the subdistribution hazards with spatial random effects.Journal of applied statistics · 2022Article
- A dependent Dirichlet process model for survival data with competing risks.Lifetime data analysis · 2021Article
- Estimation and inference for semi-competing risks based on data from a nested case-control study.Statistical methods in medical research · 2020Article
- 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
- Is mortality readmissions bias a concern for readmission rates under the Hospital Readmissions Reduction Program?Health services research · 2020Article
- Joint Shock/Death Risk Prediction Model for Patients Considering Implantable Cardioverter-Defibrillators.Circulation. Cardiovascular quality and outcomes · 2019Article
- SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.The R journal · 2019Article
- Bayesian Semiparametric Joint Regression Analysis of Recurrent Adverse Events and Survival in Esophageal Cancer Patients.The annals of applied statistics · 2019Article
- 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
- Article
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4 authors.
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Abstract
Readmission following discharge from an initial hospitalization is a key marker of quality of health care in the United States. For the most part, readmission has been studied among patients with 'acute' health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model (Normand et al., 1997). Naïve application of this model to the study of readmission among patients with 'advanced' health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk. A more appropriate analysis is to imbed such a study within the semi-competing risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semi-competing risks data. To resolve this gap in the literature we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semi-competing risks data that permits parametric or non-parametric specifications for a range of components giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters, including hospital-specific random effects. Model comparison and choice is performed via the deviance information criterion and the log-pseudo marginal likelihood statistic, both of which are based on a partially marginalized likelihood. An efficient computational scheme, based on the Metropolis-Hastings-Green algorithm, is developed and had been implemented in the SemiCompRisks R package. A comprehensive simulation study shows that the proposed framework performs very well in a range of data scenarios, and outperforms competitor analysis strategies. The proposed framework is motivated by and illustrated with an on-going study of the risk of readmission among Medicare beneficiaries diagnosed with pancreatic cancer. Using data on n=5,298 patients at
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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.