Theophilus Ogunyemi

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 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, subjects anticipation, and doctors proactivity, are found most highly significant by both methods. These six factors are potential candidates for constructing a future UI predictive index.

On s- and (m;s)-optimality of minimal incomplete block designs

In this note, we determine s- and (m,s)-optimal designs in the class of all minimal incomplete block designs under the mixed effects model.

Bayesian Prediction of Incontinence among Older Women Using an Experimental Design Template

In this study, a Bayesian predictor of urinary incontinence (UI) is devised for screening older women. Risk factors identified from an epidemiological survey data as significant for UI, are utilized. The proposed Bayesian method combines an experimental design template with relevant information to construct a predictive index in terms of posterior probabilities. The computations are carried out on a longitudinal data called the Medical, Epidemiological and Social Aspects of Aging (MESA). The index is applied to the baseline and follow-up portions of the MESA data. The results show that, the percentage of the absolute relative change between the prior and posterior probabilities can be used as a decision tool to make conclusions on credibility of the class labels on continence and incontinence. The proposed index can be applied for immediate screening and for predicting future urinary incontinence in older women of comparable demographics as those presented in the MESA data.

Preface to data mining in biomedical informatics and healthcare

In the last decade, healthcare institutions, pharmaceutical companies as well as other organizations started to aggregate biomedical and clinical data in electronic databases. Mining these databases gives promising new threads of knowledge that could lead to a variety of beneficial outcomes for the entire community, from improving patients’ quality of life towards saving public healthcare costs or increasing efficiency of private healthcare companies. Given the complexity of biomedical and clinical information, it is important to make use of the proper tools to gain valuable insights from the available data.

Skip pattern analysis of the MESA data for stratification

Urinary Incontinence (UI) is a costly condition that decreases the quality of a patients life and social engagement. Identification of UI risk factors may help early prevention and treatment of the condition. In this study we revisited the Medical, Epidemiological and Social Aspects of Aging (MESA) data collected in 1983. The experiments are conducted on a longitudinal dataset pertaining to the female-only population. A methodology that identifies skip patterns in order to facilitate MESA risk factor analysis is presented. The identified skip patterns are used to stratify MESA data. Based on the stratification performed, the important risk factors are then analyzed for each group of subjects. JRip rule extraction technique is utilized to determine the UI risk factors. Consequently, taking female hormones was determined as the most important stratifying feature. The dataset is then stratified to two subsets based on this stratifying feature. Education level, hearing problems, urine loss while coughing or sneezing, physical activity, stress and cancer are risk factors specific to taking female hormones. The common risk factors among both of the stratified groups were: stress, frequent sneezing, and low physical activity. Although there were common risk factors among both of the stratified groups these preliminary results show that different group of subjects have different risk factors, and therefore they should be provided with different UI predictive indices, diagnoses and possibly treatment plans.

Skip pattern analysis for stratification and detection of undetermined and inconsistent data

Urinary Incontinence (UI) is a costly condition that decreases the quality of a patients life and social engagement. Identification of UI risk factors may help early prevention and treatment of the condition. In this study we revisited the Medical, Epidemiological and Social Aspects of Aging (MESA) data collected in 1983 by the University of Michigan. The experiments are conducted on the dataset pertaining to the female-only population. The dataset contains missing values. First, the missing values are classified into inconsistent, undetermined, genuine missing values and skip patterns. The undetermined and inconsistent values are distinguished from the skip patterns and removed from the dataset. Once the skip patterns are detected, they are used to stratify the MESA data. Based on the stratification performed, the important risk factors are then analyzed for each group of subjects. JRip rule extraction technique is utilized to determine the UI risk factors. Consequently, taking female hormones was determined as the most important stratifying feature. The dataset is then stratified to two subsets based on this stratifying feature. Education level, hearing problems, urine loss while coughing or sneezing, physical activity, stress and cancer are risk factors specific to taking female hormones. The common risk factors among both of the stratified groups were: stress, frequent sneezing, and low physical activity. Although there were common risk factors among both of the stratified groups these preliminary results show that different group of subjects have different risk factors, and therefore they should be provided with different diagnoses and possibly treatment plans.

A novel rule infusion technique for generating simulated binary data to validate data mining methods

Mathematical analysis of existing data mining methods is not straightforward and in many cases it is not possible. Therefore, simulated data plays a central role in validation of data mining results in a given situation, i.e., noise, missing value and multicollinearity levels. This paper proposes a longitudinal binary data simulation focusing on presentation of the major challenge of infusing user-defined rules. Results of applying Apriori, PRAT, Prism, and JRip rule extraction methods on these simulated data in several missing value levels are presented in this paper. This simulation proved to be essential in verifying data mining results that we have generated on Medical Epidemiological and Social Aspects of Aging (MESA) data set.

Skip pattern analysis for detection of undetermined and inconsistent data

A common problem in clinical survey trials is missing data. Skip patterns are one type of missing data in medical datasets, skipping a respondent over a group of questions that is not relevant to them. Applying any imputation technique to missing values caused by skip patterns may add misinformation. Moreover, skip pattern analysis provides detection of non-applicable data along with undetermined and inconsistent data. The Medical, Epidemiological and Social Aspects of Aging (MESA) questionnaire is responded by a large number of subjects which entails the need of an automated method. Manual methods may not provide reliable results and they are costly. A directed, acyclic graph is generated based on the questionnaire. A graph theory method is proposed to detect each missing data type. The method finds a minimal deletion set of nodes, that are the nodes once deleted, leaves a connected graph behind. The deleted nodes can be considered as noise. The experiments are conducted on a subset of the MESA data and the results show that there are 16.04% of non-applicable data, 7.09% of genuine missing data, 0.61% of undetermined data and 0.015% of inconsistent data. This method can be used for preprocessing the dataset and estimating the noise.