Abstract Details
Activity Number:
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7
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Type:
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Invited
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Date/Time:
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Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #310875
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View Presentation
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Title:
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On the Analysis of Clustered Semi-Competing Risks Data
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Author(s):
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Sebastien Haneuse*+ and Kyu Ha Lee and Francesca Dominici and Deborah Schrag
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Companies:
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Harvard School of Public Health and Harvard School of Public Health and HSPH and Dana-Farber Cancer Institute
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Keywords:
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Survival analysis ;
Semi-competing risks ;
Pancreatic cancer
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Abstract:
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To monitor quality of care in the US, the Centers for Medicare and Medicaid Services (CMS) currently reports, among other measures, hospital-specific 30-day readmission rates, estimated on the basis of a logistic-Normal GLMM. The focus of these efforts is on health conditions with low mortality, including pneumonia and heart failure. Expanding these efforts to include a broad range of increasingly prevalent 'advanced' health conditions, such as Alzheimer's disease and cancer, is problematic because the current CMS approach ignores death as a truncating event. A more appropriate analysis would be to frame quality of care assessments within the semi-competing risk framework although, to our knowledge, no statistical methods for clustered semi-competing risks data have been developed. We propose a novel semi-parametric hierarchical model for clustered semi-competing data based on an illness-death model. Estimation and inference is within the Bayesian paradigm, which facilitates the use of hospital-specific shrinkage targets and flexible random effects distributions. An efficient computational algorithm is developed, based on the Metropolis-Hastings-Green algorithm.
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Authors who are presenting talks have a * after their name.
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