On the Analysis of Clustered Semi-Competing Risks Data

• Activity Number: 7
• Type: Invited
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Sebastien Haneuse*+ and Kyu Ha Lee and Francesca Dominici and Deborah Schrag
• Companies: Harvard School of Public Health and Harvard School of Public Health and HSPH and Dana-Farber Cancer Institute
• Keywords: Survival analysis ; Semi-competing risks ; Pancreatic cancer

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.