Clinical Trials Visualizer
Christina Bryant, Pranay Madan, Akhila Nair, Daniel Sylngstad
The Peter F. Drucker and Masatoshi Ito Graduate School of Management and Claremont Graduate University
Abstract
Dashboarding
Dashboard Views
Our tool was created by merging databases to create a visualization dashboard for
• The Clinical Trial Visualizer was built as a tool for rapid trend analysis using
users interested in rapid trend analysis of clinical trials. The tool utilizes the Clinical
Tableau 9.0
Trial Database, the Medical Subject Headings thesaurus, and Agency RSS Newsfeed.
• Users can choose from a variety of filters including year, disease, sponsor, phase,
The tool was created through Tableau to provide users with applicable visualization
type of intervention, study type, etc.
options to convey relevant trends and clinical trial topics. The dashboards are
• Custom-made views have been built to ensure maximum usability for multiple
particularly relevant for those interested in the participation options of studies or for
type of users
those interested in on going clinical trial and sponsors conducting them. The
Applications
complete tool gives users a look at diseases that are targeted by clinical trials with
information on quantity, companies that conduct them, the location of these trials,
the funding sources, the type of agencies running the trials, the phase of the trial,
and much more to give users an effective dashboard for identifying trends to answer
questions they may have regarding clinical trials. The user also have the ability to
Analyze Current Trends in
Clinical Research
Identify Gaps in Clinical
Research
The dashboard can be used to
analyze the current landscape of any
disease.
We are trying to identify gaps by
analyzing trends in clinical trial data
by focusing on certain key
therapeutic areas.
filter and cross-slice the underlying data using multiple parameters.
Objectives
Our group set out to combine clinical trial databases with a visualization platform to
create a unique dashboard that acts as tool for displaying clinical trial trends. We felt
this was important as there is a great amount of federal and industry spending on
trials.
The problem we see:
Trend Analysis
There is no tool
currently available for
rapid trend analysis
Time Consuming
Traditional techniques
are time consuming
and difficult
Loss of Insights
There are insights that
can lead saving time
and costs if identified
Our Approach
Relational Database Structure
Some questions that can be asked are:
• How many companies are
conducting trials?
• What is the status of these
trials ?
• By when can we expect results?
• Are the trials largely
government funded?
An example trend that can be seen is:
• Even though certain diseases
have higher economic burden,
there seem to be inconsistencies
in the prioritization of clinical
trials
Individuals Can Easily Locate
Trials and Enroll
And for much more…
Patients can easily visualize trials
and make informed decisions about
their health or the medication
progress
We can easily overlay more data and
perform various other analysis
References
1. Available from: http://www.clinicaltrials.gov/ct2/about-studies/learn
2. http://www.nlm.nih.gov/services/ctphases.html
3. National Institutes for Health, editor. Funding Facts - NIH Research [Internet].
[cited 2014 Apr 6]. Available from:
http://report.nih.gov/fundingfacts/fundingfacts.aspx.
4. Roumiantseva D, Carini S, Sim I, Wagner TH. Sponsorship and design
characteristics of trials registered in ClinicalTrials.gov. Contemp Clin Trials
2013;34(2):348–55.
5. Goswami ND, Pfeiffer CD, Horton JR, Chiswell K, Tasneem A, Tsalik EL. The State of
Infectious Diseases Clinical Trials: A Systematic Review of ClinicalTrials.gov. PLoS
• Initial data manipulation was performed in Microsoft Azure’s machine learning
platform, conversions resulted in missing and flawed data which were then
manually processed.
• The data was later queried and transferred into Tableau
• Database joins were performed to capture as much data as possible for the
variables we felt were significant for user display
ONE 2013;8(10):e77086.
Acknowledgement
We would like to thank Prof.Hilton and Prof. Tchalian for their constant support and
guidance through out the project. We would also like to thank Sara and Sean for
their help.