Novel application of statistical methods to identify new urinary incontinence risk factors.
Novel application of statistical methods to identify new urinary incontinence risk factors.
复制标题
统计方法的新应用来识别新的尿失禁危险因素。
DOI：
10.1155/2012/276501
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发表时间：
2012
影响因子：
1.4
通讯作者：
Diokno,AnaniasC
中科院分区：
文献类型：
--
作者：
Ogunyemi,TheophilusO;Siadat,Mohammad-Reza;Arslanturk,Suzan;Killinger,KimA;Diokno,AnaniasC
研究尿失禁（UI）危险因素的纵向数据很少。一项研究的数据，标志性的医学，流行病学和老龄化的社会方面（梅萨），在过去进行了分析，但是，重复测量分析，分析纵向数据是至关重要的，还没有应用。我们测试了一种新的应用统计方法，以确定UI的危险因素在老年妇女。梅萨数据收集在基线和每年从1955年的男性和女性在社区的样本。在每项调查中，仅对一年内回答762个基线和559个随访问题的女性进行了检查。为了测试它们在挖掘大型数据集方面的实用性，并作为创建用于开发UI的预测指数的初步步骤，在现有的梅萨数据上使用了逻辑回归、广义估计方程（GEE）和比例风险回归（PHREG）方法。GEE和PHREG组合确定了与发展UI相关的15个显著风险因素，其中6个，即尿频、尿急、任何尿量、排空后尿量、受试者的预期和医生的积极性，通过两种方法发现最显著。这六个因素是构建未来UI预测指数的潜在候选者。
Longitudinal data for studying urinary incontinence (UI) risk factors are rare. Data from one study, the hallmark Medical, Epidemiological, and Social Aspects of Aging (MESA), have been analyzed in the past; however, repeated measures analyses that are crucial for analyzing longitudinal data have not been applied. We tested a novel application of statistical methods to identify UI risk factors in older women. MESA data were collected at baseline and yearly from a sample of 1955 men and women in the community. Only women responding to the 762 baseline and 559 follow‐up questions at one year in each respective survey were examined. To test their utility in mining large data sets, and as a preliminary step to creating a predictive index for developing UI, logistic regression, generalized estimating equations (GEEs), and proportional hazard regression (PHREG) methods were used on the existing MESA data. The GEE and PHREG combination identified 15 significant risk factors associated with developing UI out of which six of them, namely, urinary frequency, urgency, any urine loss, urine loss after emptying, subject’s anticipation, and doctor’s proactivity, are found most highly significant by both methods. These six factors are potential candidates for constructing a future UI predictive index.