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18 results on '"Christian, Nicholas J."'

15. Hierarchical likelihood inference on clustered competing risks data.

Author

Christian, Nicholas J., Ha, Il Do, and Jeong, Jong ‐ Hyeon

Subjects
*ALGORITHMS, *BREAST tumors, *CLINICAL trials, *COMPUTER simulation, *DATABASES, *PROBABILITY theory, *REGRESSION analysis, *RESEARCH funding, *STATISTICS, *SYSTEM analysis, *RELATIVE medical risk, *PROPORTIONAL hazards models, *STATISTICAL models
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. [ABSTRACT FROM AUTHOR]

Published
2016
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17. Patient-Reported Outcomes in Sentinel Node–Negative Adjuvant Breast Cancer Patients Receiving Sentinel-Node Biopsy or Axillary Dissection: National Surgical Adjuvant Breast and Bowel Project Phase III Protocol B-32

Author

Land, Stephanie R., primary, Kopec, Jacek A., additional, Julian, Thomas B., additional, Brown, Ann M., additional, Anderson, Stewart J., additional, Krag, David N., additional, Christian, Nicholas J., additional, Costantino, Joseph P., additional, Wolmark, Norman, additional, and Ganz, Patricia A., additional

Published
2010
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18. Hierarchical Likelihood Inference on Clustered Competing Risks Data

Author

Christian, Nicholas J.

Abstract

Frailties models, an extension of the proportional hazards model, are used to model clustered survival data. In some situations there may be competing risks within a cluster. When this happens the basic frailty model is no longer appropriate. Depending on the purpose of the analysis, either the cause-specific hazard frailty model or the subhazard frailty model needs to be used. In this work, hierarchical likelihood (h-likelihood) methods are extended to provide a new method for fitting both types of competing risks frailty models. Methods for model selection as well as testing for covariate and clustering effects are discussed. Simulations show that in cases with little information, the h-likelihood method can perform better than the penalized partial likelihood method for estimating the subhazard frailty model. Additional simulations demonstrate that h-likelihood performs well when estimating the cause-specific hazard frailty model assuming both a univariate and bivariate frailty distribution. A real example from a breast cancer clinical trial is used to demonstrate using h-likelihood to fit both types of competing risks frailty models.Public health significance: When researchers have clustered survival data and the observations within those clusters can experience multiple types of events the popular proportional hazards model is no longer appropriate and can lead to biased estimates. For the results of a clinical study to be meaningful the estimated effects of treatments and other covariates needs to be accurate. H-likelihood methods are an alternative to existing procedures and can provide less bias and more accurate information which will ultimately lead to better patient care.

Published
2011

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