Statistical Power Analyses for Quantifying the Similarity of Categories of Surgical Procedures Among Pairs of Hospitals and Ambulatory Facilities
Notice bibliographique
Résumé
INTRODUCTION: Mixed methods are often used to understand organizational associations and differences. For example, one might compare hospitals and ambulatory surgery centers, each described by its relative distribution of cases' categories of surgical procedures, quantified using anesthesia Current Procedural Terminology (CPT) codes. The similarity of these distributions between facilities can be assessed using a metric akin to a correlation coefficient. Conceptually, identifying similar organizational pairs is feasible, as most U.S. states and Canadian provinces maintain databases containing such administrative data. However, research proposals based on mixed methods may be hindered by the lack of statistical power analysis to determine whether the quantitative phase will yield a sufficient number of similar facilities to support the qualitative phase (i.e., interviews). MATERIALS AND METHODS: Data were obtained from the American Society of Anesthesiologists' National Anesthesia Clinical Outcomes Registry. The dataset included 12,902,159 cases across 272 procedure categories, performed at 2442 facilities in the United States. The similarity index between facilities ranged from 0 (no overlap in surgical procedures) to 1 (identical distribution of procedures). Values ≥0.80 were considered indicative of high similarity. We estimated the proportion of highly similar facility pairs (similarity index ≥0.80) with low standard errors (<2.0). For each pair, we computed the inverse of the standard normal distribution based on the ratio of the difference from 0.80 to the standard error. The average of these values yielded the mean prevalence of high similarity. This estimated prevalence was then used in power analyses based on the binomial distribution. RESULTS: Only 1.00% (standard error: 0.01%) of facility pairs had a similarity index ≥0.80. Based on this prevalence, a database would need to include just 38 organizations to have an ≥80% probability of identifying at least five highly similar pairs for interviews. With data from 67 organizations, there would be a ≥95% probability of identifying at least 15 pairs. In contrast, consider an individual organization deciding whether to (a) join a consortium to identify similar organizations for shared strategies, or (b) invest in analysts to explore mandatory state or provincial databases for such purposes. Unless more than 1,000, and ideally more than 2,100, organizations contribute data, the probability of finding multiple highly similar peers may be low. CONCLUSIONS: Investigators can expect a high probability of obtaining sufficient organizational sample sizes for qualitative interviews when using large-scale databases. Although only a small fraction (approximately 1%) of organization pairs exhibit high similarity, the sheer number of potential pairs in state, provincial, and national databases compensates for this. However, for an individual organization seeking to identify peers for qualitative comparison, the chance of finding highly similar matches based on similar surgical procedures is extremely low, unless joining a very large data collective.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».