Marginal analysis of multiple outcomes with informative cluster size.

Marginal analysis of multiple outcomes with informative cluster size.
复制标题

具有信息聚类大小的多个结果的边际分析。

DOI:
10.1111/biom.13241
复制
发表时间:
2021-03
期刊:
影响因子:
1.9
通讯作者:
Nelson KP
Nelson KP
中科院分区:
数学3区
文献类型:
--
作者:
Mitani AA;Kaye EK;Nelson KP

文献摘要

参考文献

相似文献

在牙周病的监测研究中,疾病与其他健康和社会经济条件之间的关系是关键的兴趣。为了确定患者是否患有牙周病,需要在牙齿水平上进行多项临床测量(如临床附着丧失、牙槽骨丢失和牙齿活动度)。研究人员经常从这些测量中得出一个综合结果,或者分别分析每个结果。此外,患者的牙齿数量也各不相同,与口腔健康状况良好的人相比,更容易患病的人牙齿数量更少。兴趣结果与簇大小(牙齿数量)之间的这种依赖关系被称为信息簇大小,并且从拟合传统边缘模型获得的结果可能存在偏差。本文提出了一种新的方法来联合分析具有信息聚类大小的聚类数据的多个相关二进制结果,该方法使用一类具有聚类特定权重的广义估计方程(GEE)。我们使用退伍军人事务牙科纵向研究的基线数据,将我们提出的多变量结果聚类加权GEE结果与传统GEE结果进行比较。在一个广泛的模拟研究中,我们表明我们提出的方法产生的估计具有最小的相对偏差和极好的覆盖概率。
In surveillance studies of periodontal disease, the relationship between disease and other health and socioeconomic conditions is of key interest. To determine whether a patient has periodontal disease, multiple clinical measurements (eg, clinical attachment loss, alveolar bone loss, and tooth mobility) are taken at the tooth-level. Researchers often create a composite outcome from these measurements or analyze each outcome separately. Moreover, patients have varying number of teeth, with those who are more prone to the disease having fewer teeth compared to those with good oral health. Such dependence between the outcome of interest and cluster size (number of teeth) is called informative cluster size and results obtained from fitting conventional marginal models can be biased. We propose a novel method to jointly analyze multiple correlated binary outcomes for clustered data with informative cluster size using the class of generalized estimating equations (GEE) with cluster-specific weights. We compare our proposed multivariate outcome cluster-weighted GEE results to those from the convectional GEE using the baseline data from Veterans Affairs Dental Longitudinal Study. In an extensive simulation study, we show that our proposed method yields estimates with minimal relative biases and excellent coverage probabilities.
DOI: 10.1002/sim.841.abs
发表时间: 2001-07-15
影响因子: 2
作者:
Lu, M;Tilley, BC
通讯作者: Tilley, BC
DOI: 10.1002/sim.3588
发表时间: 2009-06-15
影响因子: 2
作者:
Teixeira-Pinto, Armando;Normand, Sharon-Lise T.
通讯作者: Normand, Sharon-Lise T.
DOI: 10.2190/wll4-et76-uqwn-r5fl
发表时间: 1972-01-01
期刊: AGING AND HUMAN DEVELOPMENT
影响因子: --
作者:
KAPUR, KK;GLASS, RL;FELLER, RP
通讯作者: FELLER, RP
DOI: 10.1111/biom.13050
发表时间: 2019-09-01
期刊: BIOMETRICS
影响因子: 1.9
作者:
Mitani, Aya A.;Kaye, Elizabeth K.;Nelson, Kerrie P.
通讯作者: Nelson, Kerrie P.
DOI: 10.1016/s0378-3758(98)00180-3
发表时间: 1999-02-01
影响因子: 0.9
作者:
Chaganty, NR;Shults, J
通讯作者: Shults, J