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Full Bayesian Hierarchical Logistic Regression in ACS NSQIP: Advancing Surgical Quality Benchmarking.

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Full Bayesian (FB) hierarchical logistic regression offers similar hospital performance estimates to generalized linear mixed model-empirical Bayes (GLMM-EB) in surgical quality benchmarking. FB significantly reduces computation time and enhances probabilistic interpretability for scalable benchmarking.

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Bayesian statisticsBenchmarkingNSQIPSurgical quality

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Surgical Quality Improvement

Background:

  • The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) uses hierarchical logistic regression with empirical Bayes smoothing for benchmarking.
  • This current framework faces computational burdens and potential robustness issues with sparse data at scale.

Purpose of the Study:

  • To compare the performance of full Bayesian (FB) hierarchical logistic regression with generalized linear mixed model-empirical Bayes (GLMM-EB) for ACS NSQIP benchmarking.
  • To evaluate computational efficiency and statistical concordance between the two modeling approaches.

Main Methods:

  • Utilized 2024 ACS NSQIP data encompassing 963,565 patients from 656 hospitals.
  • Fit 14 "All Cases" outcome models using both GLMM-EB and FB hierarchical logistic regression with identical predictors and hospital random intercepts.
  • Compared hospital odds ratios, outlier classification, random-intercept variance, and computation time, with secondary analysis on mortality models.

Main Results:

  • FB and GLMM-EB demonstrated high concordance in hospital estimates (log-odds correlation 0.9958) and outlier detection (Jaccard index > 0.81).
  • GPU-accelerated FB significantly reduced processing time (5.32 vs. 121.80 minutes) compared to GLMM-EB.
  • FB provided additional probabilistic interpretability, including posterior probabilities for outlier status and Bayesian false discovery rate control.

Conclusions:

  • Modern FB hierarchical logistic regression provides comparable hospital performance estimates to GLMM-EB in ACS NSQIP benchmarking.
  • FB substantially decreases computation time and enhances probabilistic interpretability, supporting its adoption for scalable surgical quality benchmarking.