1. Copyright  BioPharm Systems, Inc. 2009. All rights reserved
Leveraging
Oracle's Life
Sciences Data Hub to
Enable Dynamic Cross-
Study Analysis
Mike Grossman
VP Clinical Warehousing and
Analytics

2. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and Reuse
• Framework and LSH
• Questions and Answers
2

3. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and Reuse
• Framework and LSH
• Questions and Answers
3

4. Examples of Dynamic Analytics
• Study and Program Feasibility
– Enrollment success prediction
– Modeling around inclusion/exclusion criteria
– Cost prediction
– Investment decision support
– Marketing approach determination
• Predicting risk factors for diseases in patient
populations
– Product monitoring and risk assessment
– More focused labeling
– Modeling and simulation for portfolio management
4

5. What do we mean by Dynamic Analytics?
• Data preparation and conforming
• Data selection and analysis
• Longitudinal data mart preparation
• Model building, training/confirmation
• Applying new data to the model to obtain results
• Evaluating results, revising the model
5

6. Dynamic Analytics – Systematic
Approach
Is there a way to establish a systematic
approach to dynamic analytics so it
becomes part of the standard clinical
development processes?
6

7. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and Reuse
• Framework and LSH
• Questions and Answers
7

8. Dynamic Analytics - Overview
In this use case, Dynamic Analytics involves four stages:
– Data Preparation (Acquire, Transform, Enhance, Standardize)
– Data Selection & Preliminary Exploration
– Model Building & Analytics
– Deployment & Reuse
Preparation
Selection &
Exploration
Analytics &
Model
Building
Deployment
& Reuse
8

9. Dynamic Analytics Process
Stage 1. Data Preparation
(Acquire, Transform, Enhance, Standardize)
Historic Dataset Files
Study Data
EDC data and other
study data Data
Standardization
AE
DM …
Outcomes
Stage 3. Analytics & Model Building
Analyze, Define and
Train Model
Security
Workflow
Control Data Blinding Life Cycle Management
Workflow Management
Stage 4. Deployment & Reuse
Predictive Analysis ComponentsSelection Components
Ad hoc &
Std Analysis
Value Added
Processing
Stage 2. Select & Explore
(Acquire, Transform, Enhance, Standardize)
Selection Components
9

10. Holistic Reference, Clinical IT Reference
Architecture
Outcomes
Common Data
Model
Project level
Conformed Data
Value Added
Study Data
Conformed Study
Data
Operational Trial
Metrics
Inbound
Data
Sources
Master Meta Data
AES & Complaints
Outcomes
External Study
Data
LIMS/PK
Central Labs
CDMS/ EDC
CTMS
Staging
Area
AES & Complaints
Source Specific
Outcomes Data
Shared Study and
Project Meta
Data
Study Specific
Data Staging
Trials
Management
Warehouse
Area
Specialized Data
Marts for
Scientific
Exploration and
Mining
Specialized Data
Marts for
Scientific
Exploration and
Mining
Specialized Data
Marts for
Scientific
Exploration and
Mining
Patient Sub
Setting and
Safety
Warehouse
Clinops Data
Marts
Meta Data Libraries, Version Control, Compliance Change Mgt
Ad-Hoc Query Dashboards Structured Reports Analytical Tools
Strategic
Analysis
Regulatory
Reporting
Data Mining
Clinical
Development
Planning
10

11. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and Reuse
• Framework and LSH
• Questions and Answers
11

12. Stage 1 - Preparation
Get the data into a form which supports exploratory analysis.
This involves:
– Gathering the data
• EDC data, SAS historic data sets, other internal or external sources
– Conforming the data
• Clear understanding of the original meaning of the data
• Mapping to a standard
• Clear identification of study and subject characteristics
• Establish a library of reusable data conformance components
– Storing the data in a repository for subsequent selection and
analysis
12

13. Stage 1 – Preparation - Conforming
• Study specific conforming for EDC and other study data
• Any standard conformed structure should work
• Most companies use a modified SDTM+
• Conformed data can be used by many other parts of the
business. For example:
– Data Cleaning
– Formal status analysis
– Data listings and reporting
– CDISC SDTM
• Initially conform to the same shape and focus on the same
meaning with terminologies, such as MEDDRA and code
lists, and standard units. Expand common meaning as
goals as experience increases
13

14. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and Reuse
• Framework and LSH
• Questions and Answers
14

15. Stage 2 - Data Selection & Preliminary
Exploration
Interactively examine the data in order to gain the correct
patient population for analysis.
• Select – Subset the data based upon study and subject
characteristics in order to create an exemplar set of data to
test the hypothesis.
• Preliminary Exploration - Identify the outcome variables,
dependent variables, independent variables and domains
to be used by the analytical methods.
15

16. Stage 2 - Data Selection
• Interactive subsetting of studies and subjects
• Subset based on study characteristics and limited set of
subject domains
• Dimensional model required to increase performance and
dynamic nature of subject subsetting
• Example facts/domains for initial implementation
– Study Characteristics
– Trial Inclusion/Exclusion Criteria
– Trial Summary
– Demographics
– Exposure and Concomitant Medications
– Adverse Events/Diagnosis
16

17. Stage 2 - Data Selection – Study Star
Study
Fact
Indication
MEDDRA
Hierarchy
Study
Phase
Program
Sub-
Population
Region
Compound
(WHOD)
or Device
Design
17

18. Stage 2 - Data Selection – DM Star
DM
FACT
STUDY
SITE/REGION
GENDER
SUBJECT
RACE
AGE IN
YEARS
18

19. Stage 2 - Data Selection – CM/EX Star
EX/CM
FACT
STUDY
SUBJECT
Start Date
End Date
Drug PT
Hierarchy
Dose
Form
Route of
Admin
19

20. Stage 2 - Data Selection – AE Star
AE
FACT
STUDY
SUBJECT
Start Date
End Date
MEDDRA
PT
Hierarchy
Severity
Serious
20

21. Stage 2 - Data Selection – Shared
Dimensions
AE
FACT
MEDDRA
PT
Hierarchy
STUDY
SUBJECT
Start Date
End Date
Severity
DM
FACT
SITE/REGION
GENDER
RACE
AGE IN
YEARS
SUBJECT
STUDY
21

22. Step 2 – Data Selection Example
Dashboard
22

23. Step 2 – Data Selection Example
Dashboard
23

24. Pools
Stage 2 – Data Selection – Delivery of
Pooled Data Mart using LSH
IndividualStudies
(Domain)
MyStudyA
Subsetting
Stars
SubsettingA
StagingConforming
Dev
Dev
QC
Prod
QC
Prod
Dev
QC
Prod
MyStudyBSubsettingB
StagingConforming
Dev
QC
Prod
Dev
QC
Prod
MyStudyCSubsettingC
StagingConforming
Dev
QC
Prod
Dev
QC
Prod
Dev
QC
Prod
Dev
QC
Prod
All Datasets
Subsetted by
data selection
process
Subset NameA Subset NameB Subset NameC
24

25. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and Reuse
• Framework and LSH
• Questions and Answers
25

26. Stage 3 –
Model Building and Analytics
• Select and build a model to validate the stated
hypothesis.
• Build a set of parameterized methods that will test the
hypothesis.
• Execute the methods against the data produced in stage
two, capturing results.
26

27. Stage 4 - Deployment & Reuse
• For useful analytical methods in step three, create a set of
user accessible components that can be used with new
sets of data.
• Produce repeated results by:
– Selecting patient sub populations
– Utilizing predefined analytical methods
• Results can be stored and shared with a wider community
27

28. Stage 3,4 – Using Methods against Data
Selection
IndividualStudies
(Domain)
MyStudyA
Subsetting
Stars
SubsettingA
Pools
Subset NameA
StagingConforming
Dev
Dev
QC
Prod
QC
Prod
Dev
QC
Prod
MyStudyBSubsettingB
StagingConforming
Dev
QC
Prod
Dev
QC
Prod
MyStudyCSubsettingC
StagingConforming
Dev
QC
Prod
Dev
QC
Prod
Dev
QC
Prod
Dev
QC
Prod
All Datasets
subset by
data selection
process
Subset NameB Subset NameC
Pools
Analysis Method
Result A
Analysis Method
Result B
Libraries of
Standard and
specialty
methods
28

29. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and Reuse
• Framework and LSH
• Questions and Answers
29

30. Proposed Environment
• Overall framework for managing data, results and methods
– Oracle Life Sciences Data Hub
• Primary tool for authoring analytical methods
– SAS, Others such as R?
• Ad hoc analysis and patient population selection
– Spotfire, OBIEE, Others
• Conforming the data
– Informatica, SAS
30

31. Oracle LSH
Acquire
• Rapid acquisition of data
– No coding using reusable components
– Automatic creation of target structures from source
– Familiar use of Oracle tables and views, SAS datasets, Text files
– Automated batch loads (scheduled or triggered by message)
• Snapshots, Auditing and Security out-of the-box
• Multiple data types
– Clinical and Safety data
– PK/PD data (including blinding)
– Laboratory Data
– Pharmacoeconomic data
• Supports both warehouse and federated approaches
– Data loads
– Pass-through views
31

32. Oracle LSH
Transform, Enhance, Standardize
• Multiple parallel data models
– Standard data structures, e.g. JANUS, CDISC SDTM/ADaM, or Company Specific
– Enables evolution of data models over time
• Open technology
– Use technology best suited to purpose/skill set
• SAS, Oracle PL/SQL, Informatica
• Version control, Snapshots, Auditing and Security out-of the-box
• Multiple environments in a single application
– Development, Test, Production
• Data manipulation
– Enhance for analysis
– Pool across multiple different sources and studies
– Slice data for in-depth analysis
• Classification
– Customer-definable folder structures
– Powerful embedded search engine
32

33. Oracle LSH - Control
Security, Data Blinding, Life Cycle Management
• LSH APIS can automate complex tasks such as
– Automatically adding studies to dimensional models
– Automatically generate longitudinal data marts from subject subsets
• In-built user management and security model
– Roles and privileges
– User and user group access
– End-user administration tool
• Data blinding/unblinding
– Ensure blinding during ongoing clinical trials (GCP)
– Privileged access to blinded data
• Outputs generated on blinded data are stored in secure area
• Reusability
– All objects stored in libraries for easy re-use
• Life Cycle Management
– Designed to explicitly support SDLC according to Life Sciences regulations
• Production Areas: Cannot make destructive changes, e.g. delete tables, columns, etc.
33

34. Agenda
• Dynamic Analytics Overview
• Approach to Dynamic Analytics
• Data Preparation
• Data Selection
• Model Building, Analytics, and reuse
• Framework and LSH
• Questions and Answers
34

35. BioPharm Services for Integration and
Analytics
• Business case development and cost analysis
• Requirements and design management
• Best practice analysis and recommendations
• Installation and configuration
• Oracle CDA and LSH pilots and proofs of concept
• Hosting
• Oracle CDA and LSH implementation
• CDA and LSH validation
• CDA and LSH training
• CDA and LSH extension development
35

36. Contact Information
If you have additional questions, please contact:
United States: +1 877 654 0033
United Kingdom: +44 (0) 1865 910200
Email Address: info@biopharm.com
Website: www.biopharm.com
36