benefits of a clinically relevant library of ... - lex jansen · chart freq, pareto plot, line...
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Benefits of a Rapidly Growing Library
of Clinically Relevant
Visualization Templates/Patterns
www.i-review.com
Eric S. Herbel
President
Integrated Clinical Systems, Inc.
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Topics
▪ Trends toward data standardization
▪ Typical ongoing study – clinical data review
▪ Continually evolving ways of visualizing clinical data
▪ Importance of quick use patterns/templates
▪ Rapid addition of clinically relevant visualization
patterns/templates
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▪ Trends toward data standardization (CDISC)
FDA: Studies started after Dec 17, 2016 must use standards
in the data catalog (SDTM, ADaM, SEND, define.xml)
That means – when submitting data – for those studies –
data must be in standards based format.
https://www.fda.gov/forindustry/datastandards/studydatastandards/default.htm
Data Standardization
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▪ Significant adoption of EDC/eSource
Many sponsors have standardized on use of EDC or eSource
Medidata Rave
Oracle Inform
Omnicomm Trialmaster
Medrio
IBM/Merge
Clinical Ink
… more vendors emerging every day
Study Data Collection
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▪ EDC/eSource data structures
Typically not SDTM or standards based
-> ‘EDC Raw Data Format’
▪ Data export format (‘SAS on demand’, etc.)
Typically not SDTM or standards based
-> ‘EDC Raw Data Format’
Study Data Collection & Export
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▪ Option to transform to SDTM on export
Many EDC vendors offer that – but most charge $$$
▪ So – we’re seeing:
- sponsors converting to standard format in-house
or
- reviewing ongoing studies in ‘EDC raw data format’
then converting to SDTM later in the process
Study Data Export
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▪ Challenge of use a ‘standard approach’ to reviewing data on ongoing studies
▪ Standard Safety Review Librarybased on FDA Safety Review Guidance
2 options:
1. spend time/resources developing data transformation to SDTM upfront for each study
- or –
2. spend time defining/developing data visualizations foreach study
Data Visualization of ongoing study data
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▪ Working with many FDA medical reviewers
and sponsors customers
we see continually evolving ways of
visualizing clinical data
▪ How do we accommodate this?
Ways of Visualizing Study Data
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▪ Design a system that presents a wide selection of clinically relevant visualization templates/patterns
▪ Build in intelligence – processes and complex algorithms in the templates
▪ Keep the user interface quick, easy, simple to use.
▪ Facilitate reusability by storing definitions at higher ‘scope’/’level’ (project or ‘StudyGroup’)
Quick use patterns/templates
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▪ Many types of data/domains are verticalLabs, ECGs, Vitals, etc.
▪ Very flexible for adding content-> but really tough for reporting and graphing
▪ Define ‘Vertical to Horizontal’ transformationonce – per vertical dataset/domain & scope
▪ Use as ‘virtual horizontal’ structure thereafter
Facilitate use of common data shapes
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Vertical to Horizontal ‘Virtual Panel’
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▪ Continually evolving ways of visualizing clinical data to pick up trends, outliers, signals
▪ Recent examples FDA – ‘composite Hy’s Law chart’Sponsor customers - Waterfall Plot, Swimmer Plot, Sunburst Plot
AE Incidence table & Demographic Summary Table
▪ Previous year-FDA Cardio-Renal div – histogram for data distribution, cumulative count by time, exposure by dose, disposition percent
▪ Earlier – Napoleon's March, Benefit/Risk, Volcano Plot, AE Risk Assessment, Spaghetti Plot, Tree map/Heat map
▪ Original set – baseline vs min/max or endpoint, box whiskers, bar chart freq, pareto plot, line chart over time, bubble chart, Kaplan Meier, Timeline Trellis.
Rapid addition to clinically relevant templates
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▪ Not just – scatter plot, bar chart, line chart, etc.
▪ Graph type defined, but then clinically relevant definitions, processes, algorithms embedded in the template, i.e., baseline or endpoint or ratio of peak value to ULNR, etc.
▪ Automatically handle data shape transformations – to be able to easily work with vertically shaped data.
▪ Easy to use user interface – drag and drop – to select which variables to use in the template.
▪ Build in the complexities necessary for the visualization –but keep it simple for the user!
Characteristics of clinically relevant templates
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Hy’s Law Chart – drag & drop UI
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1. Labs -> Vert->Horz presentation
2. ULNR known
3. Max post baseline per patient
4. Plot on log scale
5. Draw Clinically Relevant threshold
6. Count patients in each
Hy’s law quadrant / by var
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Composite Hy’s Law Chart – drag & drop UI
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Composite Hy’s Law Chart
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1. Labs -> Vert->Horz presentation
2. ULNR known
3. Determine baseline Hy’s Law Quadrant
4. Determine Max post baseline per patient
5. Plot value of Ratio of Max / baseline per patient
Plot value for patient in appropriate quadrant
4. Plot on log scale
5. Draw 1x, 2x, 3x (of ratio) reference lines
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Waterfall Plot
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Swimmer Lane Plot
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Demographic Summary Table
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AE Incidence Table
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AE Incidence TableCutoff 2% or greater
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▪ Integrate with many clinical data sources
▪ Dynamic multi-study pooling
▪ Built-in ‘patient identification/drill down’
▪ Patient review tracking & report review notes
▪ Many clinically relevant graphics, tabulations, patient profiles, risk assessments, etc. including defining critical data
▪ Graph Patient Profiles – directly access study data (no need for external data setup) – run-time day calculations
▪ Built in codelist/sas format awareness
▪ SAS/R program integration
▪ Patient Narratives
Other Important Considerations
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Dashboard Views in JReview
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Dashboard Views in JReview
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Report Reviewer Notes
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▪ Report Notes – review exception listings – editable
review status, action, comments – stored separately
▪ When data updates (typically nightly) – notes are
retained – unless have option to reset status when
any data changes on the report line.
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▪ Use of these clinically relevant templates – with specified graph type, variables, filters
-> saved definition -> library (at study, project or studyGroup)
▪ As quick as it is to use a quick use template –selecting variables of interest & filters
-> it’s quicker to re-execute a saved definition!
-> and published to a dashboard view.
Standard Library of Definitions
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▪ Either access data directly in clinical data source- or –Register SAS Datasets (with SAS Share)- or –Load SAS datasets or ODM-XML -> database
-> register or load new study – loadingstudy meta data automatically
-> about 10 minutes
▪ If totally new study/data structures->define new set of standard library objects->about 2-3 hours for about 100 object definitions
▪ If a new study, but based on previous non-standard definitions – inherit from project or studygroup
Typical Process
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Thank You!
Any questions or comments?
Eric S. Herbel
Integrated Clinical Systems, Inc.
(908) 996-3312
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