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Advances in Urology
Volume 2012 (2012), Article ID 276501, 8 pages
Research Article

Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors

1Department of Mathematics and Statistics, Oakland University, 2200 N. Squirrel Road, Rochester, MI 48309, USA
2Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA
3Beaumont Health System, William Beaumont Hospital, Royal Oak, MI 48073, USA
4Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA

Received 19 July 2012; Revised 25 September 2012; Accepted 25 September 2012

Academic Editor: Miroslav L. Djordjevic

Copyright © 2012 Theophilus O. Ogunyemi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.