Hierarchical Models for Semicompeting Risks Data With Application to Quality of End-of-Life Care for Pancreatic Cancer

• 成果类型: Article
• 署名作者: Lee, Kyu Ha; Dominici, Francesca; Schrag, Deborah; Haneuse, Sebastien
• 署名单位: Harvard University; Harvard University Medical Affiliates; Forsyth Institute; Harvard University; Harvard School of Dental Medicine; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1164052
• 发表日期: 2016
• 页码: 1075-1095
• 关键词: semi-competing risks; survival-data; frailty models; medicare beneficiaries; multistate models; READMISSION RATES; bayesian-analysis; hazard functions; UNITED-STATES; regression

摘要

Readmission following discharge from an initial hospitalization is a key marker of quality of healthcare in the United States. For the most part, readmission has been studied among patients with acute health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model. Naive application of this model to the study of readmission among patients with advanced health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk. A more appropriate analysis is to imbed such a study within the semicompeting risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semicompeting risks data. To resolve this gap in the literature we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semicompeting risks data that permits parametric or non parametric specifications for a range of components giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters, including hospital specific random effects. Model comparison and choice is performed via the deviance information criterion and the log-pseudo marginal likelihood statistic, both of which are based on a partially marginalized likelihood. An efficient computational scheme, based on the Metropolis-Hastings-Green algorithm, is developed and had been implemented in the R package SemiCompRisks. A comprehensive simulation study shows that the proposed framework performs very well in a range of data scenarios, and outperforms competitor analysis strategies. The proposed framework is motivated by and illustrated with an ongoing study of the risk of readmission among Medicare beneficiaries diagnosed with pancreatic cancer. Using data on n = 5298 patients at J=112 hospitals in the six New England states between 2000-2009, key scientific questions we consider include the role of patient-level risk factors on the risk of readmission and the extent of variation in risk across hospitals not explained by differences in patient case-mix. Supplementary materials for this article are available online.