Abstract:
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. The proposed framework is then applied to data on all individuals diagnosed with pancreatic cancer between 2005-2008 from Medicare Part A.

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