Statistical profiling methods with hierarchical logistic regression for healthcare providers with binary outcomes

Xiaowei Yang, Bin Peng, Rongqi Chen, Qian Zhang, Dianwen Zhu, Qing J. Zhang, Fuzhong Xue, Lihong Qi

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Within the context of California's public report of coronary artery bypass graft (CABG) surgery outcomes, we first thoroughly review popular statistical methods for profiling healthcare providers. Extensive simulation studies are then conducted to compare profiling schemes based on hierarchical logistic regression (LR) modeling under various conditions. Both Bayesian and frequentist's methods are evaluated in classifying hospitals into 'better', 'normal' or 'worse' service providers. The simulation results suggest that no single method would dominate others on all accounts. Traditional schemes based on LR tend to identify too many false outliers, while those based on hierarchical modeling are relatively conservative. The issue of over shrinkage in hierarchical modeling is also investigated using the 2005-2006 California CABG data set. The article provides theoretical and empirical evidence in choosing the right methodology for provider profiling.

Original languageEnglish (US)
Pages (from-to)46-59
Number of pages14
JournalJournal of Applied Statistics
Volume41
Issue number1
DOIs
StatePublished - Jan 2014

Keywords

  • Bayesian mixed-effects models
  • hierarchical logistic regression models
  • provider profiling
  • quality of care
  • risk-adjusted mortality

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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