Intermediate and advanced topics in multilevel logistic regression analysis.
Intermediate and advanced topics in multilevel logistic regression analysis.
复制标题
多级逻辑回归分析中的中级和高级主题。
DOI：
10.1002/sim.7336
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发表时间：
2017-09-10
影响因子：
2
通讯作者：
Merlo J
中科院分区：
文献类型：
--
作者：
Austin PC;Merlo J
多层次数据经常出现在卫生服务、人口和公共卫生以及流行病学研究中。在这类研究中，两种结果很常见。多水平Logistic回归模型允许在估计受试者和聚集性特征对受试者结果的影响时，考虑到受试者在较高级别单元集群内的聚集性。对PubMed数据库的搜索表明，多水平或分层回归模型的使用正在迅速增加。然而，我们的印象是，许多分析师只是使用多水平回归模型来解释集群引起的集群内同质性的滋扰。在这篇文章中，我们描述了一套分析，可以补充多水平Logistic回归模型的拟合。这些辅助分析使分析人员能够估计在对象和群组水平上测量的协变量的边际或总体平均影响，而不是原始多水平Logistic回归模型产生的群内或群组特定影响。我们描述了区间优势比和相对优势比的比例，它们是聚类级协变量影响的综合衡量标准。我们描述了方差分配系数和中位优势比，它们是对结果中的方差和异质性分量的度量。这些措施使人们能够量化一般背景影响的大小。我们描述了一种R2度量，它允许分析师量化不同多水平Logistic回归模型所解释的变异比例。我们通过分析确诊为急性心肌梗死住院患者的死亡率来说明这些措施的应用和解释。©2017作者。约翰·威利父子有限公司出版的医学统计数据。
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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影响因子：
1.9
作者：
Larsen, K;Petersen, JH;Endahl, L
通讯作者：
Endahl, L
影响因子：
3.7
作者：
Ghith N;Wagner P;Frølich A;Merlo J
通讯作者：
Merlo J
影响因子：
2.7
作者：
Heagerty, PJ;Kurland, BF
通讯作者：
Kurland, BF
DOI：
10.1080/03610910802361366
发表时间：
2008-01-01
影响因子：
0.9
作者：
Li, Jialiang;Gray, Brian R.;Bates, Douglas M.
通讯作者：
Bates, Douglas M.
影响因子：
6.3
作者：
Merlo, J;Chaix, B;Råstam, L
通讯作者：
Råstam, L