Literature DB >> 33311840

Performance Characteristics of Profiling Methods and the Impact of Inadequate Case-mix Adjustment.

Yanjun Chen1, Damla Şentürk2, Jason P Estes3, Luis F Campos4, Connie M Rhee5, Lorien S Dalrymple6, Kamyar Kalantar-Zadeh5, Danh V Nguyen7.   

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.

Entities:  

Keywords:  fixed effects; hierarchical logistic regression; profiling analysis; random effects

Year:  2019        PMID: 33311840      PMCID: PMC7731974          DOI: 10.1080/03610918.2019.1595649

Source DB:  PubMed          Journal:  Commun Stat Simul Comput        ISSN: 0361-0918            Impact factor:   1.118


  2 in total

1.  Fixed Effects High-Dimensional Profiling Models in Low Information Context.

Authors:  Jason P Estes; Damla Şentürk; Esra Kürüm; Connie M Rhee; Danh V Nguyen
Journal:  Int J Stat Med Res       Date:  2021-09-27

2.  Modelling hospital outcome: problems with endogeneity.

Authors:  John L Moran; John D Santamaria; Graeme J Duke
Journal:  BMC Med Res Methodol       Date:  2021-06-21       Impact factor: 4.615

  2 in total

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