A comparison of a Bayesian vs. a frequentist method for profiling hospital performance
Bibliographic record
Abstract
The objective of this study was to compare the classification of hospitals as outcomes outliers using a commonly implemented frequentist statistical approach vs. an implementation of Bayesian hierarchical statistical models, using 30-day hospital-level mortality rates for a cohort of acute myocardial infarction patients as a test case. For the frequentist approach, a logistic regression model was constructed to predict mortality. For each hospital, a risk-adj usted mortality rate was computed. Those hospitals whose 95% confidence interval, around the risk-adjusted mortality rate, excludes the mean mortality rate were classified as outliers. With the Bayesian hierarchical models, three factors could vary: the profile of the typical patient (low, medium or high risk), the extent to which the mortality rate for the typical patient departed from average, and the probability that the mortality rate was indeed different by the specified amount. The agreement between the two methods was compared for different patient profiles, threshold differences from the average and probabilities. Only marginal agreement was shown between the Bayesian and frequentist approaches. In only five of the 27 comparisons was the kappa statistic at least 0.40. The remaining 22 comparisons demonstrated only marginal agreement between the two methods. Within the Bayesian framework, hospital classification clearly depended on patient profile, threshold and probability of exceeding the threshold. These inconsistencies raise questions about the validity of current methods for classifying hospital performance, and suggest a need for urgent research into which methods are most meaningful to clinicians, managers and the general public.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".