hierarchical modelling framework for identifying unusual performance in health care providers

A wide variety of statistical methods have been proposed for detecting unusual performance in cross-sectional data on health care providers. We attempt to create a unified framework for comparing these methods, focusing on a clear distinction between estimation and hypothesis testing approaches, wit...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 2007-01, Vol.170 (4), p.865-890
Hauptverfasser:	Ohlssen, David I, Sharples, Linda D, Spiegelhalter, David J
Format:	Artikel
Sprache:	eng
Schlagworte:	Applications Bayesian method Biology, psychology, social sciences Cross-validation Data analysis Data collection Divergent performance Exact sciences and technology General topics Health care Health Care Services Health outcomes Health services Hierarchy Hypothesis testing Institutional comparisons Markovian processes Mathematics Medical sciences Modeling Monte Carlo simulation Multilevel models Multiple comparisons Null hypothesis Outliers P values Parametric inference Parametric models Probability and statistics Random effects Sciences and techniques of general use Simulations Statistical analysis Statistical methods Statistics Surgeons
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!