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 language | English (US) |
|---|---|
| Pages (from-to) | 46-59 |
| Number of pages | 14 |
| Journal | Journal of Applied Statistics |
| Volume | 41 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2014 |
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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
Cite this
Statistical profiling methods with hierarchical logistic regression for healthcare providers with binary outcomes. / Yang, Xiaowei; Peng, Bin; Chen, Rongqi; Zhang, Qian; Zhu, Dianwen; Zhang, Qing J.; Xue, Fuzhong; Qi, Lihong.
In: Journal of Applied Statistics, Vol. 41, No. 1, 01.2014, p. 46-59.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Statistical profiling methods with hierarchical logistic regression for healthcare providers with binary outcomes
AU - Yang, Xiaowei
AU - Peng, Bin
AU - Chen, Rongqi
AU - Zhang, Qian
AU - Zhu, Dianwen
AU - Zhang, Qing J.
AU - Xue, Fuzhong
AU - Qi, Lihong
PY - 2014/1
Y1 - 2014/1
N2 - 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.
AB - 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.
KW - Bayesian mixed-effects models
KW - hierarchical logistic regression models
KW - provider profiling
KW - quality of care
KW - risk-adjusted mortality
UR - http://www.scopus.com/inward/record.url?scp=84889097831&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889097831&partnerID=8YFLogxK
U2 - 10.1080/02664763.2013.830086
DO - 10.1080/02664763.2013.830086
M3 - Article
AN - SCOPUS:84889097831
VL - 41
SP - 46
EP - 59
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
IS - 1
ER -