Stratification of Clinical Survey Data by Using Contingency Tables

Abstract

Data stratification is the process of partitioning the data into distinct and non-overlapping groups since the study population consists of subpopulations that are of particular interest. In clinical data, once the data is stratified into sub populations based on a significant stratifying factor, different risk factors can be determined from each subpopulation. In this paper, the Fisher’s Exact Test is used to determine the significant stratifying factors. The experiments are conducted on a simulated study and the Medical, Epidemiological and Social Aspects of Aging (MESA) data constructed for prediction of urinary incontinence. Results show that, smoking is the most significant stratifying factor of MESA data, showing that the smokers and non-smokers indicates different risk factors towards urinary incontinence and should be treated differently.