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

Abstract

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.