Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors
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 no...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Advances in Urology |
| Online Access: | http://dx.doi.org/10.1155/2012/276501 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832564219840561152 |
|---|---|
| author | Theophilus O. Ogunyemi Mohammad-Reza Siadat Suzan Arslanturk Kim A. Killinger Ananias C. Diokno |
| author_facet | Theophilus O. Ogunyemi Mohammad-Reza Siadat Suzan Arslanturk Kim A. Killinger Ananias C. Diokno |
| author_sort | Theophilus O. Ogunyemi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba5bf2214226474391b2de4fc5fc809e |
| institution | Kabale University |
| issn | 1687-6369 1687-6377 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Urology |
| spelling | doaj-art-ba5bf2214226474391b2de4fc5fc809e2025-02-03T01:11:38ZengWileyAdvances in Urology1687-63691687-63772012-01-01201210.1155/2012/276501276501Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk FactorsTheophilus O. Ogunyemi0Mohammad-Reza Siadat1Suzan Arslanturk2Kim A. Killinger3Ananias C. Diokno4Department of Mathematics and Statistics, Oakland University, 2200 N. Squirrel Road, Rochester, MI 48309, USADepartment of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USADepartment of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USABeaumont Health System, William Beaumont Hospital, Royal Oak, MI 48073, USABeaumont Health System, William Beaumont Hospital, Royal Oak, MI 48073, USALongitudinal 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.http://dx.doi.org/10.1155/2012/276501 |
| spellingShingle | Theophilus O. Ogunyemi Mohammad-Reza Siadat Suzan Arslanturk Kim A. Killinger Ananias C. Diokno Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors Advances in Urology |
| title | Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors |
| title_full | Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors |
| title_fullStr | Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors |
| title_full_unstemmed | Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors |
| title_short | Novel Application of Statistical Methods to Identify New Urinary Incontinence Risk Factors |
| title_sort | novel application of statistical methods to identify new urinary incontinence risk factors |
| url | http://dx.doi.org/10.1155/2012/276501 |
| work_keys_str_mv | AT theophilusoogunyemi novelapplicationofstatisticalmethodstoidentifynewurinaryincontinenceriskfactors AT mohammadrezasiadat novelapplicationofstatisticalmethodstoidentifynewurinaryincontinenceriskfactors AT suzanarslanturk novelapplicationofstatisticalmethodstoidentifynewurinaryincontinenceriskfactors AT kimakillinger novelapplicationofstatisticalmethodstoidentifynewurinaryincontinenceriskfactors AT ananiascdiokno novelapplicationofstatisticalmethodstoidentifynewurinaryincontinenceriskfactors |