基于线性混合模型,使用加权和自校准预测因子标记异常群集。

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-03-27 DOI:10.1093/biomtc/ujae022
Charles E McCulloch, John M Neuhaus, Ross D Boylan
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引用次数: 0

摘要

包含特定群组截距的统计模型常用于分层环境,例如,观察结果集中在患者内部或患者集中在医院内部。这些截距的预测值通常用于识别或 "标记 "极端或离群群组,如表现不佳的医院或健康状况急剧下降的患者。我们考虑了多种标记规则,评估了不同的预测因子,并采用了不同的准确度测量方法。通过理论计算和全面的数值评估,我们发现以前提出的基于两种最常用预测因子(通常的最佳线性无偏预测因子和固定效应预测因子)的规则表现极差:错误标记率要么高得令人无法接受(接近 0.5 的极限值),要么过于保守(例如,远远低于 0.5)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors.

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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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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