Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis.

In the USA, the Centers for Medicare and Medicaid Services use 30-day readmission, following hospitalization, as a proxy outcome to monitor quality of care. These efforts generally focus on treatable health conditions, such as pneumonia and heart failure. Expanding quality-of-care systems to monitor conditions for which treatment options are limited or non-existent, such as pancreatic cancer, is challenging because of the non-trivial force of mortality; 30-day mortality for pancreatic cancer is approximately 30%. In the statistical literature, data that arise when the observation of the time to some non-terminal event is subject to some terminal event are referred to as 'semicompeting risks data'. Given such data, scientific interest may lie in at least one of three areas: estimation or inference for regression parameters, characterization of dependence between the two events and prediction given a covariate profile. Existing statistical methods focus almost exclusively on the first of these; methods are sparse or non-existent, however, when interest lies with understanding dependence and performing prediction. We propose a Bayesian semiparametric regression framework for analysing semicompeting risks data that permits the simultaneous investigation of all three of the aforementioned scientific goals. Characterization of the induced posterior and posterior predictive distributions is achieved via an efficient Metropolis-Hastings-Green algorithm, which has been implemented in an R package. The framework proposed is applied to data on 16051 individuals who were diagnosed with pancreatic cancer between 2005 and 2008, obtained from Medicare part A. We found that increased risk for readmission is associated with a high comorbidity index, a long hospital stay at initial hospitalization, non-white race, being male and discharge to home care. [ABSTRACT FROM AUTHOR]