Usman Iqbal M.D, MPH, MBA Senior Med AFF Leader Global Medical Affairs

AstraZeneca

BIG DATA design and Application: Efficiency of Data

Analysis and Stakeholder Application

Aug 2014

Outline

• Big DATA outreach

• KEY Methodological considerations in Data Application & Interpretation

• Examples/Case Studies of BIG DATA application

R&D, Commercial/Patient Access

• Summary

3

BIG DATA outreach has transformed all phases of Product lifecycle: Preclinical, Clinical, Real World Evidence

(RWE)

Laboratory Research

Population-based Clinical Research

Patient-oriented Clinical Research

Clinical Trials

Treatment Impact • Clinical improvement

(efficacy/effectiveness • Safety improvement (tolerability) • Functional / Symptom

improvement (Patient Reported Outcomes)

Disease •Disease Severity / Burden / Progression

Economic Impact •Medication-Related •Non-Medication Related •Incremental Cost-Effectiveness •C&R Limitations

Target Population Identification

• Clinical Guidelines (place in therapy)

• Prevalence / Incidence / Size • Unmet Medical Needs

Real World Context (RWE)

EFFICACY EFFECTIVENESS

Characteristic in Efficacy & Effectiveness setting

Characteristic Efficacy Effectiveness

Patients Tight inclusion/exclusion criteria, few co-morbid

conditions Broad patient populations, average number of

co-morbid conditions

Physician Academicians, opinion leaders Community-based physicians

Incentives Clinicians pay for recruitment, trial managed

patients receive free care

ACA, usual reimbursement per visit, co-pays. coinsurance

Protocol of care Frequent, systematic visits with tight follow-up Infrequent visits with limited systematic follow-

up

Monitoring Pill counts, outbound calls Refill reminders from pharmacies

Technology Electronic capture of self-reported diary entries

with interactive outbound calls Web-based tools

Information

Informed consent, explicit and extensive education regarding the risks and benefits of

therapy

No informed consent, limited patient education

Care team beyond physicians

Routine study monitors monitoring for confusion about the drug, adherence issues & adverse

effects Pharmacists observing refill frequency

Mayo Clin Proc. • April 2011;86(4):268-270 • doi:10.4065/mcp.2011.0123 •

www.mayoclinicproceedings.com

BIG Data Application in RWE Context

• Real world data is informative in understanding and supporting the medical value for the 3Ps (Patient, Physician, Payers) during the developmental, launch and LCM phases

Domains Key Considerations for the Relative Medical Value

Disease Disease Description

Burden of Disease

Target Population Epidemiology

Relative Treatment Impact

Disease Management

Unmet Needs

Therapeutic Alternative

Product Profile

Clinical Efficacy & Safety

Economic Impact Health Economics, Coverage & reimbursement

landscape assessment

High quality &

representative

real world data

is essential in

preparing the

evidence to

support

product

development

BIG DATA Definition in the eye of RWE : Depending on the disease, many real world data options may be available, each with its own set of pros and

cons

Data Type Readily available? Representative of

population?

Tailored to specific questions?

Rx/Medical claims More likely

National electronic medical records

(EMR) Somewhat likely

Chart extractions Less likely

Registries Depends Less likely

Less likely

More likely

More likely

Practice-based networks platforms combine all of the data options at a

unique patient identifier level to map the FULL PATIENT JOURNEY

Test of Association in RWE-BIG DATA Assessments needs to PASS the SB and CBI

E D B A

Causal

C

E D

B A

Confounding

C

E D

B A

Selection bias

2. Confounding By Indication ( CBI)

An open non-causal path without colliders

3. Selection bias (SB)

A non-causal path that is open due to conditioning on a collider

BCVs?

Bias creates an association that is not true, but confounding describes an

association that is true, but potentially misleading

Confounding by Indication Example- Schematics (From Gordis)

Bias creates an association that is not true, but confounding describes an

association that is true, but potentially misleading

Confounding variable has to be linked with both exposure AND disease

CBI Scenarios in BIG DATA comparisons -Patients with increased disease severity predisposed to getting branded vs. generics -Expensive drug being pushed to the last formulary tier reserved for “hard to manage” patients

• Selective differences between comparison groups that can impact the association between exposure and outcomes

– Patient Demographics

– Disease characteristics

Bias creates an association that is not true, but confounding describes an

association that is true, but potentially misleading

Selection Bias: An important concept to address for in BIG DATA- RWE Analytics

Detailed Knowledge of the Patient Journey is required to conduct reliable BIG DATA studies

Confidential and Proprietary – No Reproduction Without Prior Permission – IMS © 2012

12

PBN Platform leverages all available data to link patient information from all sources of care

History from 1999

1.0B claims/year

870,000 practitioners/month

National/sub-national

Medical/Office/ Clinic

- Age, gender, ZIP3

- Geography

- ICD-9 diagnosis

- CPT procedure

- HCPCS product

- Date of service

- Location of care

- Reported cost of service

- Payers

- Payer types

- Referring practitioner

- Actual paid cost of service by payer

- Longitudinal de-ID patients

History from 2001

Over 1.6B Rx/year

Retail and specialty

Pharmacy/

Prescription

- National - Prescriber - Pharmacies - Payer types - Products

- Age, gender, 3-digit ZIP

- Rx date written, filled

- Quantity dispensed - Days supply

- Refill, new, switch - Cost information

- Plan - Retail, non-retail,

mail, specialty

- Longitudinal de-ID patients

History from 2001

650+ hospitals

7MM inpatient stays

60MM outpatient visits

Inpatient/Outpatient Hospital Charge Master

- Hospital ID - Hospital

characteristics - Age, gender, ZIP3 - Source of admission - ICD-9 diagnosis - CPT procedure - HCPCS product

- Product use & units - Day of stay

- Locations of care - Payer types

- Reported changes - Length of stay

- Discharge month & disposition

- Longitudinal de-ID patients

Oncology EMR

History from 1997

500,000+ Patients

790 Physicians

74/327

Sites/Locations

Detailed Clinical Data

Urology EMR

40 urology practices

EMR Electronic Medical Records

- Age, gender, ZIP3 - Cancer type - Comorbidities - Drug regimens

- Metastases/staging - Radiation - Lab values - Oral meds,

Injectables - Dosing

- Length of therapy - Treatment intervals

- Tumor type - Transfusions/

transplants - Surgery/diagnostic

testing - Longitudinal de-ID

patients

Test Results,

Lab Values,

Dates

Hospital Visits,

Dates,

Service Details

Tumor Stage

Pathology Results

& Dates

Pharmacy &

Chemotherapy Drug,

Dose, Date of Service

Outpatient Services,

DOS, Transfusions,

Physician Services

Linked

Across

All Sites

of Care

Sales forecast revised based on foundational epidemiological data

Competitors actual Co-pay and PAS Intelligence

Database cross fertilization/ resource optimization for market research

US EAP sample estimation revised from n=400 to 100 based on JAKAFI Heat Map Data

Foundational epidemiology, economic burden of disease & cost drivers

ASH Pubs: 3 posters, 1 oral

Treatment Pattern Analyses

Solu

tion

Impact (to date)

Situation Oncology entry into a rare disease with limited real world knowledge of “natural history of disease, target populations, treatment patterns & patient flows, competitor landscape & unmet needs”

Gap Compelling need for real world data to inform Product Differentiation, provide Evidence Based Value Proposition and Strategic Decision Support for JAK2

Leveraging Cutting Edge Health IT infrastructure

Acquisition & customization of multiple data sets: Community Practices, Academic Centers of Excellence, Registries

Patient level EMR, Rx, Claims, Hospitalization, Outpatient Data linked into a single platform

Largest Longitudinal US/EU MF Real World Data Hub

US Sample N= 6,500 MF

Joint Collaboration with Oncology Clin Dev and Med Aff

Real World Comparative Effectiveness Research (CER)

Broad Based Matrix decision support via Strategic Excellence in Research

Solution

R&D EVD Medical Affairs Commercial

Site Optimization revised for MF Ph-2 Study targeting High Jakafi prescribing facilities

Investigator outreach campaign based on MF provider heat map data

100 + Community Practices

Sanofi Myeloproliferative Neoplasms (MPN) BIG DATA platform: Quest for Strategic excellence In RWE research & application

Hess et al. Characteristics of Patients included in the Myelofibrosis Real-World Practice Based Network Research Data Platform. Blood, November 15, 2013; Blood : 122 (21)

From Impact of Big Data –RWE application in rare diseases (Myelofibrosis: Epidemiology, Burden of the Disease, Sales

Forecasting)

Foundational epidemiology,

economic burden of disease & cost

drivers

12

BIG DATA RWE Application to R&D and Launch Planning

• Evaluate and Understand treatment patterns to inform protocol feasibility

• Utilization of real-world observational data to inform protocol development and value based outcomes for key stakeholders

• Evidence based insights to determine unmet needs and patient success

• Identify and locate key patient populations for R&D study design and recruitment

• Evidence based competitive positioning & early commercial decision support – Patient Flows, Prescribing Patterns, KOL profiling, Co-pay Structures,

METHODS OF ANALYSIS

FOR PHYSICIAN SOCIAL

NETWORKS

Central Individual

Community 1

Community 2

Community 3

• Clinical influence based on

behavioral impact out to 3

degrees

• Clinical influence based on

Communities of Practice

• A community is a group of

individuals who are more

connected to each other

than to outsiders

• Where information flow &

behavior change is

strongest

Measuring clinical influence

An integrated network

Combining Software, BIG Data and Analytics for Unparalleled Results for Physician engagement

• Physician Influence Networks Identifies influence patterns in HCP networks and predicts individual physicians overall value in prescribing practices (Influence Index™)

• KOL Identification Provides a more comprehensive and cost-effective way to identify and manage thought leaders using secondary data and analytics

• Proven case studies in specialty care management: strategic insight, commercial decision support, patient access

Economic

Outcomes

Clinical

Outcomes Cloud-based

supercomputing

Patient

Characteristics Treatments

Integrating and Leveraging the Patient “Data Stack” Available data sources for building predictive models

Predictive Models

Does patient X respond

to treatment Y?

Pharmacy and Medical Claims

100M lives

Aggregated EMR Data

25M lives

Trial Data

Biotech Co., DFCI, etc.

Registry Data

Thousands of lives

-omic Data

SNPs, NGS

Simulations

CONFIDENTIAL DISCOVERING WHAT WORKS. AND FOR WHOM.

Volume Velocity

Variety

Scop

e U

PI

Integratio

n

Summary • BIG DATA in RWE is more about scope, strength,

generalizability and UPI integration

• Understanding methodological issues around BIG DATA analyses and interpretation is vital

• PBN networks are a great stride forward in mapping the patient journey and allowing reliable RWE research

• A scalable approach to BIG DATA development through PBN development helps manage expectations and enhance ROI

• BIG DATA driven KOL mapping provides a more comprehensive and cost-effective way to commercial engagement and optimizing patient access

• RCT based BIG DATA platforms are different in dynamics than RWE ones

BIG DATA learnings can effectively influence both product development as well as commercialization