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Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors.
McCulloch, Charles E; Neuhaus, John M; Boylan, Ross D.
Afiliación
  • McCulloch CE; Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco 94158, United States.
  • Neuhaus JM; Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco 94158, United States.
  • Boylan RD; Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco 94158, United States.
Biometrics ; 80(2)2024 Mar 27.
Article en En | MEDLINE | ID: mdl-38563530
ABSTRACT
Statistical models incorporating cluster-specific intercepts are commonly used in hierarchical settings, for example, observations clustered within patients or patients clustered within hospitals. Predicted values of these intercepts are often used to identify or "flag" extreme or outlying clusters, such as poorly performing hospitals or patients with rapid declines in their health. We consider a variety of flagging rules, assessing different predictors, and using different accuracy measures. Using theoretical calculations and comprehensive numerical evaluation, we show that previously proposed rules based on the 2 most commonly used predictors, the usual best linear unbiased predictor and fixed effects predictor, perform extremely poorly the incorrect flagging rates are either unacceptably high (approaching 0.5 in the limit) or overly conservative (eg, much <0.05 for reasonable parameter values, leading to very low correct flagging rates). We develop novel methods for flagging extreme clusters that can control the incorrect flagging rates, including very simple-to-use versions that we call "self-calibrated." The new methods have substantially higher correct flagging rates than previously proposed methods for flagging extreme values, while controlling the incorrect flagging rates. We illustrate their application using data on length of stay in pediatric hospitals for children admitted for asthma diagnoses.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Asma / Modelos Estadísticos Límite: Child / Humans Idioma: En Revista: Biometrics Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Asma / Modelos Estadísticos Límite: Child / Humans Idioma: En Revista: Biometrics Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos