1400 Saha-Oncology Market Assessment Using APLD · 2015. 4. 23. · In this presentation, we will show how patient ‐level ... Microsoft PowerPoint - 1400 Saha-Oncology Market Assessment

Market Assessment in Oncology using APLD

Agenda for today’s discussion

• Oncology and APLD

• Defining the market using APLD

• Understanding treatment regimens using APLD

• Triangulating APLD with other data sources

• Case Study

• Q&A

Oncology market has evolved rapidly in the last few years, and so has the complexity of treatment …

• Market for cancer treatments is one of the largest and fastest growing in the pharma industry

• Treatment environment has improved substantially – a number of treatment options available for physicians today (targeted therapies, novelty drugs, diagnostic tests, genetic testing, etc.)

• However, this has also led to increase in complexity of treatment process

• Significant implications on sales & marketing for Oncology drugs

Treatment options and complexity pose significant challenges for sales/marketing analytics

Challenges• Products have multiple

indications across cancer types• Indications are typically for

specific treatment regimens or line of therapy

• Products used frequently in combination therapies

• Physicians may opt to use products in unknown ways

• Products administered as Infused, IV (in hospital /office settings) or oral (specialty drugs)

What is my product use across different cancer types?

What is the market opportunity for a specific indication and LOT?

Who are my key competitors, by indication and LOT ?

How do I evaluate my most important customers ?

What is my most optimal targeting strategy ?

Key Business Issues

These questions have been historically challenging to answer in the field of Oncology. We will address them in this document.

Traditional pharma data sources are inadequate for Oncology sales/marketing analytics

• Traditional pharma data (e.g. retail pharmacy, distribution, payer, etc.) have limitations: – Retail data doesn’t capture product use (Oncology drugs mostly infused, injected or dispensed via specialty pharma)

– Distribution data provides only aggregate estimate of product demand, not use by tumor type

– Doesn’t provide product use by regimen or line of therapy

Demand Data

Fortunately, patient‐level data (APLD) can provide the required answers

• Sourced from Medical and Rx claims,

• Provides longitudinal history of diagnoses, medical procedures and Rx for patients

• Linked to physicians who made the diagnosis and performed the procedures

In this presentation, we will show how patient‐level data & analytics can help answer key business questions in Oncology

However, patient‐level data (APLD) has challenges …

• Missing patient or physician information– Large patient sample sizes, but

incomplete physician/patient info in ways that is not very clear

– Imperfect match b/w medical and Rx claims

– Changing sample over time in ‘open panel’ patient data

• Limited depth and detail of information for patient treatment

– Diagnostic/Lab test results not available – Doesn’t specify all treatment information

(e.g. LOT, combo vs mono, etc.)

APLD data alone cannot be used for sales/marketing analytics

‐ Useful for directional analysis, not actual estimates

‐ Other reliable data sources need to be augmented to get the true picture

ImplicationChallenges






• Case Study

• Q&A

Defining and determining the ‘market’

• Oncology drugs have very specific indications– Example: Herceptin is effective in

treatment of HER2+ breast cancer

• Too many patients in broader market, not useful for analysis

• Lots of false positives in data; need to filter ‘genuine’ patients

• APLD provides diagnosis /treatment info, but may not have lab results

Longitudinal patient history in APLD can help in defining a market based on trends in patient treatment

1. Start with a broad market basket of patients

2. Consult disease state experts to understand typical patient journey and treatment protocols

3. Analyze patient level data (i.e. diagnosis, procedures, Rx, etc.) to identify patient treatment trends

4. Develop business rules to narrow down to meaningful patients of interest

Example: Oral product X indicated for 3 types of cancers, but used widely across several cancer types

Situation

• X is indicated for 3 types cancers ‐A, B and C.

• Need to assess Brand X utilization in cancer market A

• APLD has too many patients with diagnosis A, most aren’t ‘definitive’ patients

Key Questions

• How do we define market for A ?

• What is the market opportunity for brand X for cancer type A?

• What is brand X utilization in A ?

Solution






• Case Study

• Q&A

Understanding treatment regimens and progression in Oncology

• Drugs are indicated and used for specific line of therapy (LOT)

• Drugs administered usually in combination with other products

– For e.g. common therapy for treatment of non‐Hodgkin Lymphoma includes CHOP (combination of cyclophosphamide, doxorubicin, oncovin and prednisone)

• Important to understand patient progression, line of treatment and regimens for accurate market assessment

Treatment windows, Wash‐out periods and Progression

Treatment Window• A period where all treatments given to a patient are considered part of the same therapy/regimen

Wash‐out Period• A period with no treatment administered to a newly diagnosed patient, to prepare the patient for a new cancer treatment

Treatment Progression• Change in treatment LOT, typically when patient has developed immunity or resistance to earlier treatment

A

Diagnosis/Treatment in Oncology

X Y B X D

Washo

ut Period

Treatment Window 1

Treatment Window 2

Treatment windows, wash‐out periods and change in therapies can be used to determine progress of patient b/w regimens or LOTs

New

Diagn

osed

Washout Period

Depending on type of cancer, changes in treatment can help identify regimens and LOT progression

Case Situation

• Product X indicated for cancer “ABC” for 2nd line treatment

• X occasionally used for 1st line treatment as well, but Y only used in 2nd line

• Products P and Q typically given in combo as 1st line treatment only; never in 2nd line

Key Questions

• How to identify patients by LOT 1st vs. 2nd ?

• How many 2nd line patients are treated with Product X ?

• How many 1st line patients are being treated with product X?

Diagnosis ABC

Solution

Treat P

Consider 2 patients who are diagnosed with “ABC” cancer

Treat Q

Treat X

Diagnosis ABC

Patient still in 1st line

Patient has progressed to 2nd line

Treat P

Treat P

Treat Q

Treat Y

Treat X



• Identifying a defined market using APLD



• Case Study

• Q&A

APLD data cannot be used standalone for sales and marketing analytics

• APLD data has missing medical or Rx claims, as well as missing patients

– Missing data varies physician to physician

• APLD data useful for: – Directional analysis (e.g. patient counts

over time, product use ?)– Mix of product use (e.g. how much is a

product used across indications ?)– Relative calculations (e.g. what %age of

patients treated with a given product?)

• For deterministic analysis, APLD should be triangulated with other data sources

Non‐retail demand data

APLD data

Aggregate product (infused) use in hospital

Product # Units in Demand

P1 159.6K

P2 189.3K

P3 15.5K

Product # Units in APLD % Capture

P1 74.8K 47%

P2 41.9K 22%

P3 8.1K 53%

Triangulation: Leveraging other data sources with APLD to generate reliable estimates

Data from more robust data sources, viz. prescriber, demand, etc. can be used in conjunction with APLD to generate more “reliable” estimates.

APLD(Diagnosis, Px, Rx, Units)

Lab Data (Clinical results, diagnostic info,

etc.)

Non‐Retail Demand

(Account‐level units)

Retail Prescriptions(Physician Rx) Closed panel

patient data (e.g. EMR,

SiteAlerts, etc.)

Other …

Triangulation approach using APLD and demand data (non‐retail) for IV product (indicated across tumor types)

Non‐retail Demand DataAPLD Data project

Establish measure of overall

treatment volume

Estimate patient volume by disease

state

Choose products correlated to overall patient volumes for relevant disease states

Determine product mix using APLD

Estimate size of patient

populations

Determine unique diagnosed patients by tumor type, calculate proportion of each

Reverse‐engineer to get volume estimates based on knowledge of treatment protocols and available intelligence on product use

Further triangulation to size high‐potential opportunities, e.g., patients with specific treatment pattern, etc.






• Case Study

• Q&A

Objective: Estimate market opportunity and brand penetration for Oncology brand using APLD

Situation• Oncology IV brand indicated for 3

different tumor types, and specific regimens within each tumor type

• Use of brand and opportunity across different tumors not very well understood

• Dissatisfaction amongst Sales Reps, questioning opportunity at territory level

• Wide variations in growth by sales territory

Key Questions

• How can we measure market opportunity and brand penetration at an account‐level for each tumor type?

• How can this information be used to better allocate resources?

• How can we identify the physicians treating patients where is there is highest opportunity ?

High‐level metrics used for market opportunity and brand penetration (brand and competition)

Product (All markets)*

Market(1)**

Market(2)**

Market(3)**+ +

=Product Penetration (Overall)

Overall Brand Penetration:

Penetration by Tumor Type:

Product (1)*

Market(1)**

=Product Penetration (1) Similar definitions for

market (2) and (3)

Compute at various levels of granularity:

Nation ‐> Geography ‐> Account ‐> Physician

* Unique product treated patients for indicated tumor type during defined time period

** Unique patients diagnosed with indicated tumor type who are candidates for product

Illustration 1: Account level detailed information of market opportunity, brand performance, ranking, etc. Sales Reps can see patient counts (opportunity) and brand performance for an account, both overall and by indicated tumor type. They can also compare account performance against benchmarks for districts/nation

Illustration 2: Compare brand performance with competition at the account levelSales Reps can see patient counts and brand performance for an account, both overall and by indicated tumor type

Illustration 3: Compare potential and performance across different accounts

Sales Reps can compare market opportunity and brand penetration between different accounts, to determine which accounts need their highest attention






• Case Study

• Q&A

Q&A

Jennifer MaurerAssociate Director, Sales OperationsCelgeneEmail: [email protected]

Sudeep SahaDirectorAxtria Inc. Email: [email protected]

Documents

1400 Saha-Oncology Market Assessment Using APLD · 2015. 4. 23. · In this presentation, we will show how patient ‐level ... Microsoft PowerPoint - 1400 Saha-Oncology Market Assessment