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Analysis Group, Inc. Health Economics, Outcomes Research, and Epidemiology Practice Areas

September 13, 2012

Analysis Group is the largest privately held economic consulting firm in North America

Our clients include major law firms, Fortune 100 companies, and government agencies

We have been growing steadily since our launch over 30 years ago and now have 10 offices in the U.S. and Canada

Our firm has over 500 professionals, many with graduate degrees in economics, finance, accounting, management, or law

We have been ranked as one of the top ten consulting firms by Vault for the past three years

We have been named one of the ‘Top Places to Work’ in Massachusetts by The Boston Globe for the past four years

A Snapshot of Our Firm

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Interdisciplinary team with expertise in: Health economics General economics Econometrics Biostatistics Epidemiology Pharmacy Medicine Finance Business Administration Accounting

Our Professional Team

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Over 120 health care specialists with advanced degrees: PhD, PharmD, MD, and ScD

Pharmaceutical manufacturers

Medical device manufacturers

Drug delivery companies

Biotechnology companies

Payers (insurers, employers)

General and specialty hospitals, integrated delivery networks, joint ventures, and physician practices

Equipment and medical product companies

State and federal government

Our Clients

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Our Integrated Approach to Health Care

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COMMERCIAL RESULTS

Brand strategy and positioning

Market analysis and segmentation

Global pricing and reimbursement

Managed care contracting Competitive assessment Lifecycle and patent expiry

management strategy

SCIENTIFIC OUTCOMES

R&D health care economics Health outcomes and cost-

effectiveness Econometrics Public/regulatory hearings Epidemiology and

biostatistics Clinical trials optimization

BUSINESS DIRECTION

Therapeutic area selection and strategy

Product portfolio optimization Partnering, licensing,

outsourcing, acquisitions Organizational structuring to

achieve growth Technology and

commercialization assessment

LITIGATION Intellectual property Antitrust Product fraud Off-label investigations General commercial

business litigation Contract disputes

Selected AG Health Care Studies in the News Votrient February 22, 2011 AG epidemiologists and Harvard affiliates used novel statistical methods to assess survival data for economic models demonstrating Votrient’s cost-effectiveness. NICE issued a positive Final Appraisal Determination for Votrient as a first-line treatment for advanced or metastatic renal cell carcinoma.

May 2, 2007 When the maker of the blockbuster antidepressant Celexa was threatened with safety concerns and additional black box warnings by the U.S. FDA, AG epidemiologists helped demonstrate that the drug was no less safe than class competitors and averted differential labeling.

Humira June 2008 AG pharmacoeconomists provided the maker of Humira with pivotal support in modeling and developing health economic submission packages for commercial and national payers. NICE has recommended Humira and not competing TNFs in certain indications.

Celexa

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Our Research Generates Timely and Impactful Scientific Evidence

Gaps in Scientific Research

Decision Maker Needs

Client Needs

Innovative research

Cutting-edge health data

World-class analytics

Academic thought leaders

Publications

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Analysis Group Assists Health Care Clients with HEOR Challenges Over the Product Life Cycle

Early stage modeling and GAP analysis

Personalized medicine analysis to help refocus clinical trial

Increase awareness of diseases (e.g., prevalence, burden of illness)

Identify, design, and validate appropriate PRO instrument

Treatment patterns of conventional therapy (adherence, dose escalation)

Drug safety

Limitations of standard care and competitors (unmet needs)

Demonstrate product value relative to conventional therapy

Cost of illness studies

HTA/reimbursement submission: CEA, BIA, Dossier

Treatment pattern and adherence

Support market expansion (burden of under-treatment, delayed treatment and delayed diagnosis)

Identify the patient subgroup populations who most likely to benefit from a given treatment

Discovery and Development

Product Approval and

Launch

Post-Launch Optimization

Patent Expiry and Late Life

Cycle

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Examples of Our Work:

Matching-Adjusted Indirect Comparisons

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Health care decision makers face choices among a growing number of alternative treatments

Policy makers recognize the importance of strengthening the evidence base for medical decisions (e.g., renewed U.S. public investment in CER)

Comparative evidence is valuable, but difficult to obtain

Head-to-head randomized trials provide the gold standard, but are not always available, especially for new drugs

A costly gap can result if treatment decisions and product strategies are developed without accounting for true product superiorities

Indirect Comparisons in Comparative Effectiveness Research

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Indirect comparisons help to fill such gaps

Make a fair and credible comparison Need to adjust for differences between trials

Provide timely results Need to inform decisions as they are made; can’t wait for more

data

Draw robust conclusions Use all available clinical trial data Appreciated by payers

Goals of Indirect Comparison

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Inclusion/exclusion criteria for comparator trial need to be equivalent to or nested within the inclusion criteria for the IPD trial

Step 1: Study and Sample Selection

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A vs. placebo (IPD available) B vs. placebo (published trial)

Increasing Disease Severity Increasing Disease Severity

Incr

easi

ng A

ge

Incr

easi

ng A

ge

Inclusion/exclusion criteria for the published comparator trial can then be imposed on the IPD to create comparable populations

Step 1: Study and Sample Selection

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A vs. placebo (IPD available) B vs. placebo (published trial)

Increasing Disease Severity Increasing Disease Severity

Incr

easi

ng A

ge

Incr

easi

ng A

ge

Using robust statistical methodology, patients in Trial A can be re-weighted to match the baseline characteristics of those in Trial B

Step 2: Reweighting Patients

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0

10

20

30

40

50

60

Males Females

%

010203040506070

Males Females

%

010203040506070

Males Females

%

Increase weight for females relative to males

Trial of A vs. placebo Trial of B vs. placebo Re-weighted Trial of A vs. placebo

Results: Comparison of Outcomes between Matched Trials

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Our drug’s efficacy after matching

Competitor drug’s published efficacy

Balanced placebo-arm efficacy from our trial (after matching) and the competitor’s trial (as published)

P < 0.001

Mea

n Im

prov

emen

t in

Sym

ptom

s

Examples of Our Work:

Personalized Medicine

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Comparative Effectiveness Research (CER): “What treatment works best for which patient population?”

Traditional CER: Given a patient population, find the treatment that works best Individualized CER: Given a treatment, find the patient population for whom it works best

Personalized Medicine

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Any subpopulation involves a tradeoff between two goals Payers do not systematically consider this tradeoff when designing access

restrictions The optimal subpopulation is the largest subpopulation in which the value

proposition is acceptable to payers

Identification of Optimal Patient Subpopulations

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Maximize value proposition Payers want to get the most value while limiting costs

Maximize market size We want to access the largest market consistent with labeling

How can we identify this optimal subpopulation?

In the full trial population, 21% more patients achieve response with active drug vs. placebo

Identification of Optimal Patient Subpopulations

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0 10 20 30 40 50 60 70 80 90

100

20 40 60 80 100

E

ffica

cy

Subpopulation Size

All Patients

Hypothetical data

Subpopulations defined by specific patient characteristics may show greater efficacy but may have limited market potential

Identification of Optimal Patient Subpopulations

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0 10 20 30 40 50 60 70 80 90

100

20 40 60 80 100

E

ffica

cy

Subpopulation Size

All Patients

≥ 2 prior therapies Age < 50

Weight < 100 kg

Hypothetical data

By combining multiple patient characteristics, the personalized medicine approach identifies the largest market size at any level of efficiency (the efficiency frontier)

Identification of Optimal Patient Subpopulations

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0 10 20 30 40 50 60 70 80 90

100

20 40 60 80 100

Ef

ficac

y

Subpopulation Size

All Patients

≥ 2 prior therapies Age < 50

Weight < 100 kg

Efficiency Frontier

Hypothetical data

Examples of Our Work:

Drug Adherence

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Impact of Non-Adherence to Anti-Epileptic Drugs on Health Care Utilization Resources and Mortality

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Source: Duh MS, et al., ISPOR 2008; Duh MS, et al., ISPE 2008.

1.39

1.76

1.19

3.32

0.93

0

0.5

1

1.5

2

2.5

3

3.5

Hospitalizations Inpatient Days EmergencyRoom Visits

Outpatient Visits Mortality

Inci

denc

e Ra

te R

atio

Haza

rd R

atio

Examples of Our Work:

Cost of Illness Studies

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Annual Costs per ADHD Patient and Non-ADHD Family Member

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$0

$1,000

$2,000

$3,000

Control Patients ADHD Patients Family Members inControl Family

Sample

Non-ADHD FamilyMembers in ADHD

Family Sample

Cos

t per

Yea

r

Hospital Outpatient Prescription Drug Hospital Inpatient Provider's Office Work Loss Other

$541

$1,574 $1,289

$2,495

ADHD Patients Non-ADHD Family Members

Source: Swensen A, Birnbaum H, et al., J Am Acad Child Adoles Psychiatry 2003; 42(12) 1415-1423.

Your Career at Analysis Group

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Career advancement is based on individual contributions in distinct areas:

Career progression reflects:

Casework

Project Management

Business Development

Teamwork

Overall contribution to the firm

Your Career Path - Positions

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Analyst Senior Analyst Associate Manager Vice

President Managing Principal

Ability to work across practice areas

Exposure to different industries and areas of expertise

A supportive environment where you will grow professionally:

Advisors and peer-mentors

Flexible case assignments

Collaborative/open-door policy

Lack of hierarchy

Highly motivated, highly skilled colleagues

Work closely with managing principals and academic affiliates; interact directly with clients

What Can You Expect as an Associate at Analysis Group?

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Advanced degree in quantitative sciences, such as health economics, biostatistics, econometrics, statistics, epidemiology, psychometrics

Outstanding track record of applying quantitative methods to real-world research problems, preferably in health care research

Proficiency in at least one statistical programming language (e.g., SAS, R, S-PLUS)

Excellent organizational and communication skills

Comfortable interacting with clients and key opinion leaders

Ability to work independently and with a team

An Ideal Candidate

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Interest in health economics, outcomes research, and epidemiology

Your academic experience and career goals

Industry experience or specialized expertise

Data-driven papers or projects you’ve worked on

Analytical and technical skills; conceptual capabilities

Communication skills and teamwork experiences

Displays of leadership

Your Interview: What We Want to Know About You

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Analysis Group, Inc. is currently conducting a resume drop for Johns Hopkins Bloomberg School of Public Health students. Resume Submission Deadline: Monday, October 15, 2012

Please submit your resume, unofficial transcript and a cover letter indicating geographic preference(s) through the Career Services office and the Analysis Group, Inc. website: www.analysisgroup.com/open_positions. In order to be considered, you MUST list Career Services/Job Posting in the source field. For more information about Analysis Group, please visit our website at www.analysisgroup.com

Application Information

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