Performance characteristics of profiling methods and the impact of inadequate case-mix adjustment

Yanjun Chen, Damla Şentürk, Jason P. Estes, Luis F. Campos, Connie M. Rhee, Lorien Dalrymple, Kamyar Kalantar-Zadeh, Danh V. Nguyen

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider’s performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

Original languageEnglish (US)
JournalCommunications in Statistics: Simulation and Computation
DOIs
StatePublished - Jan 1 2019
Externally publishedYes

Keywords

  • Fixed effects
  • Hierarchical logistic regression
  • Profiling analysis
  • Random effects

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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