leveraging oracle's life sciences data hub to enable dynamic cross-study analysis

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

Agenda

• Dynamic Analytics Overview

• Approach to Dynamic Analytics

• Data Preparation

• Data Selection

• Model Building, Analytics, and Reuse

• Framework and LSH

• Questions and Answers

2

Agenda




• Data Selection




3

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

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

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

Agenda




• Data Selection




7

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

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 Components Selection Components

Ad hoc &

Std Analysis

Value Added

Processing

Stage 2. Select & Explore (Acquire, Transform, Enhance, Standardize)

Selection Components

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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

Agenda




• Data Selection




11

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

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

Agenda




• Data Selection




14

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

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

Stage 2 - Data Selection – Study Star

Study Fact

Indication MEDDRA

Hierarchy

Study

Phase

Program

Sub-Population

Region

Compound (WHOD) or Device

Design

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Stage 2 - Data Selection – DM Star

DM FACT

STUDY

SITE/REGION

GENDER

SUBJECT

RACE

AGE IN YEARS

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Stage 2 - Data Selection – CM/EX Star

EX/CM FACT

STUDY

SUBJECT

Start Date

End Date Drug PT

Hierarchy

Dose Form

Route of Admin

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Stage 2 - Data Selection – AE Star

AE FACT

STUDY

SUBJECT

Start Date

End Date MEDDRA

PT Hierarchy

Severity

Serious

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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

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Step 2 – Data Selection Example Dashboard

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Step 2 – Data Selection Example Dashboard

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Po

ols

Stage 2 – Data Selection – Delivery of Pooled Data Mart using LSH

Ind

ivid

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(Do

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Prod

All Datasets

Subsetted by

data selection

process Subset NameA Subset NameB Subset NameC

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Agenda




• Data Selection




25

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.

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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

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Stage 3,4 – Using Methods against Data Selection

Ind

ivid

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ies

(Do

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Subset NameA

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Dev

QC

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All Datasets

subset by

data selection

process

Subset NameB Subset NameC

Po

ols

Analysis Method

Result A Analysis Method

Result B

Libraries of

Standard and

specialty

methods

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Agenda




• Data Selection




29

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

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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

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

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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.

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Agenda




• Data Selection

• Model Building, Analytics, and reuse



34

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

Contact Information

If you have additional questions, please contact:

United States: +1 877 654 0033

United Kingdom: +44 (0) 1865 910200

Email Address: [email protected]

Website: www.biopharm.com

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leveraging oracle's life sciences data hub to enable dynamic cross-study analysis

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