Utilizing R Shiny to Create a Statistical Dataview: University of Pennsylvania Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP)

To help better understand the underlying causes of the two most prominent chronic urological pain disorders – interstitial cystitis/bladder pain syndrome (IC/BPS) and chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) established the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network in 2008. The primary clinical research effort carried out during the MAPP Network's first 5-year project period (MAPP I) was a prospective cohort study and from December 14, 2009, through December 14, 2012, 1,039 men and women were enrolled in the study, including persons with UCPPS (n = 424); persons with other comorbid illnesses, including fibromyalgia, irritable bowel syndrome, and chronic fatigue syndrome (n = 200 for all conditions); and healthy controls (n = 415). All study participants were extensively characterized (i.e., phenotyped) at baseline, and UCPPS participants were further assessed during an additional 12-month follow-up period.

In order for researchers to gain unlimited access to the raw data from the MAPP studies, a MAPP Dataview application was created using R Shiny. This applet allows users to query data and view or download these results in tabular or graphic representation. The Dataview is optimized for user interaction, incorporating different graphical and statistical options for the user to obtain summary statistics. These options consist of a wide range of graphs and summary tables on either the MAPP full dataset or user-specified subsets. Users can additionally run basic regression analysis on the baseline data for both continuous and categorical data. Four datasets are available for use: (1) MAPP I baseline, (2) MAPP II baseline, (3) MAPP I longitudinal, and (4) MAPP II longitudinal and the Dataview database, which is updated regularly to incorporate newly obtained follow-up data.

Data Visualization with R Shiny

MAPP Dataview

Chronic prostatitis/chronic pelvic pain syndrome

User Generated Graphs, Tables and Summary Statistics

Multidisciplinary Approach to the Study of Chronic Pelvic Pain

Interstitial cystitis/bladder pain syndrome 

Mid-Level

Data Visualization

Symposium on Data Science and Statistics (SDSS) 2023