David cocker feasibility_and_web_mining
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Transcript of David cocker feasibility_and_web_mining
Data Mining to Make Global
Feasibility Assessment More
Reliable
David J. Cocker,
Senior Partner
MDCPartners, Belgium
This presentation
• Evolving clinical trial landscape
• Information newly available via the internet
• Public data sources to enhance feasibility
reliability.
Data Mining Disclosure
• Can we leverage these expanding public
data sources?
• To fix these poor assumptions
Leverage information
Number trials
Pubs Ratio
Five year lag
Evolution of trial registry and publication ratio
Normalized publication
count
Trial start Pubs Registered
An avalanche of
new information
will descend upon
us
Feasibility on Feasibility
However, with a relatively sophisticated industry approach to
knowledge management, metrics and analysis…
Why do we get this so wrong, so often?
Classic problem but there is a classic solution
Time
Expenditure
Recru
itm
ent
Planned
Invest in in-depth feasibility
Delay
Opportunity cost
Over-run
%
%
Y1 Y2
Throwing more money at feasibility.
Will it improve reliability?
Problem
1
Problem
2
Bad assumptions still plague Pharma
Internal Clinical team assumptions
10 subjects per site
4 subjects per site
Scanned 750 trials,
60,000 patient mass
Two year delay
Company added another 67 sites
76 sites to recruit 750 patients
Need 188 sites to recruit 750 patients
Meta-analysis outcomes
A study in diffuse large B cell lymphoma subjects who recently completed R-CHOP therapy.
The simplest meta-analysis of a trial
registry would have mitigated this poor
initial assumption.
Applying meta-analysis to classic questions
Protocol
Patients with
the diseaseWhere do they live?
Number required
Selection of site
Selection criteriaAccess
ExperienceEquipment
Go
Country selection
Sites in area
which may be
suitable
The Environmental Trial Conveyor Belt
Experience
Equipment
Feasibility My trial is rolling
New
Studies
Publication
pre-emptionRetention
Regulatory
Drug
Supply
The practice
We cannot escape a rolling feasibility process
Hard points
• Number of eligible patients expected to recruit
• Concurrent trial workload, particularly at recruitment stage
• Previous experience in similar clinical studies
• Recruitment & retention in prior clinical trials
• Site personnel study experience and training
• Trial-required facilities such as laboratories and pharmacies
Private historical data
Enrolment history
Start-up dynamics
Country performance
In-house predictive modeling tools
Predictions
Estimations
Meta-
Evidence
Internal KPI
History
Predictive modeling and
decision support tools
Survey data solicited from
potential sites
Best
Guess
Global transparency
Global trial activity
Academic literature
Disclosure
What’s out on the net and what’s to come?
• Regulatory push, societal expectation
– Sunshine Act and payments to healthcare professionals
– Clinical trial registries and result synopses
– Journal editors requiring registration
– Institutional review committees and procedures
Conclusion
More disclosure, more transparency, more to come!
Data Relationship and Semantics
Semantics System
Clinical
trial
Registry
FDA, EUHospital
Directory Commercial
Web portal
Pharmaceutical
company
Ad hoc
Web
Information
Conference
seminar
Chaos
Published
Investigator
Medline
OrderWorld demographics
+
Male Female
It’s not just about clinical research disclosure. It’s about the reality of internet
information linkied up.
Identifying experienced individuals in organizations
MeSH
Therapy relevance
Impact
factor
60 1 302 30
Key data elements of the The power of semantic web disambiguation
Investigator
Site
Sponsor
Condition
Drugs
Trials
Treatment
use
A better view of the environment without the emotion
Investigator
Subject
Travelling
Distance(134
Km)
Incidence (189,000)
Screening
Failure (16746)
Female (189,000)
Age (167,456)
Popu
lation p
ool availa
bili
ty
Site load for area 770/
55 sites
Subject enrollment target 700
Breast Cancer Phase ll
Population Pool (210,000,000)
Classify system to research questions
Who
What
When
Where
Investigator
Site
Sponsor
ConditionDrugs
Trials
Treatment
use
Information that is on the move, stays on the
move. Monitor and re-visit often.
Number of investigators - 220
Number of investigators - 96
Regional population – 3,500,000
Essen as a region
Regional population – 7,500,000
Berlin
Investigator (score)
Investigator (score)
Trial Count (score)
Trial Count (score)
Investigator (score)Let the robot do the
legwork, and then debate
the assumptions.
Visualization of clinical trial registries
25
Disambiguating a trial registry can
render a nice picture
Rituximab sites
Breast Cancer sites
United Kingdom
Germany
Belgium
France
Traffic light system to
indicate site
availability
Site location
Estimated
enrollment
histogram
Organization
score based on
internet
footprint
Trial
experience in
years
Average
patients per site
Absolute number of
patients per site
accounting for
incidence, catchment
radius and screening
failure
Competing sites in
catchment area
based on site
criteria
Can you answer Questions
Ranking data, even if qualitative, allows a better
basis for discussion than a crystal ball.
Navigating complex interdependencies
Medical need The model is under stress
Better communicationMore trust
Conclusions
• An automated and rolling corporate engagement in site evaluation
and ranking.
• Mash-up and visualize all available data not just your own.
• Exploit expanding disclosure data as a tangible return on investment
for your participation.
• Validate your historic data with more dynamic data.
• Confirm assumptions through more targeted sampling based on
internet meta-analysis.
• Expand cross industry KPIs.
David J. Cocker
Senior Partner
Product Specialist Clinical Business Intelligence
Systems
MDCPartners cvba
Vluchtenburgstraat 5 2630 Aartselaar – Belgium
Office +32 (0) 3 870 97 50
Direct +32 (0) 3 870 97 72
Fax +32(0) 3 870 97 51
www.mdcpartners.be
Product www.ta-scan.com
Thank you