shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area

Author:

Xia Qing1ORCID,Mudaranthakam Dinesh Pal12ORCID,Chollet-Hinton Lynn12ORCID,Chen Ronald23,Krebill Hope2ORCID,Kuo Hanluen2ORCID,Koestler Devin C.12ORCID

Affiliation:

1. Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS

2. The University of Kansas Cancer Center, Kansas City, KS

3. Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS

Abstract

PURPOSE The University of Kansas Cancer Center (KU Cancer Center) recently developed a data warehouse to Organize and Prioritize Trends to Inform KU Cancer Center (OPTIK). The OPTIK database aggregates and standardizes data collected across the bistate catchment area served by the KU Cancer Center. To improve the usability of the OPTIK database, we developed shinyOPTIK, a user-friendly, interactive web application for visualizing cancer risk factor and mortality rate data across the KU Cancer Center Catchment area. METHODS Data in the OPTIK database were first consolidated at the county level across the KU Cancer Center catchment area. Next, the shinyOPTIK development team met with the KU Cancer Center leadership to discuss the needs and priorities of the shinyOPTIK web application. shinyOPTIK was developed under the R Shiny framework and consists of a user interface (ui.R) and a web server (server.R). At present, s hinyOPTIK can be used to generate county-level geographical heatmaps; bar plots of demographic, screening, and risk factors; and line plots to visualize temporal trends at different Rural-Urban Continuum Codes (RUCCs), rural-urban status, metropolitan, or county levels across the KU Cancer Center catchment area. RESULTS Two examples, adult obesity prevalence and lung cancer mortality, are presented to illustrate how researchers can use shinyOPTIK. Each example is accompanied by post hoc visualizations to help explain key observations in terms of rural-urban disparities. CONCLUSION Although shinyOPTIK was developed to improve understanding of spatial and temporal trends across the population served by the KU Cancer Center, our hope is that the description of the steps involved in the creation of this tool along with open-source code for our application provided herein will serve as a guide for other research centers in the development of similar tools.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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