appropriate use of ims...

Appropriate Use of IMS InformationFinancial Community PresentationNovember 12, 2009

Meeting Objective

•Helping the Financial Community to understand how to optimize use of IMS offerings

−Share processes and methodologies for the IMS national audits that will aid in better understanding how to interpret publishedestimates

−Familiarize you with a few best practice approaches that will enhance your ability to utilize IMS data effectively

2 Confidential and Proprietary to IMS, November 2009

Meeting Topics

• Comparison of National Databases

• Applying Tools and Resources

• Applying Best Practices for Cross-Audit Comparisons

• Impact of Marketplace Pharmacy Practices

• Other Offerings to support Appropriate Use of IMS data


Comparison of IMS National DatabasesKey Audit Distinctions

MANUFACTURER WAREHOUSE

RX

NON RETAIL OUTLETS

LONG TERM CAREPHARMACIES

RETAIL/MAIL PHARMACIES

Supply

(DDD™/NATIONAL SALES PERSPECTIVES™)

Demand

(Xponent™/NATIONAL PRESCRIPTION AUDIT™)

DIRECT SALES

DIRECT SALES

INDIRECT SALES

Healthcare Map

Flow of Sales and Prescriptions through the Healthcare Market

R

R

RX/APLD

RX


The Prescription Database

The IMS US prescription database is a representative sample of Retail (Chain, Foodstore, Independent pharmacies), Mail Service and Long Term Care pharmacies.

• Measures both national and sub national dispensed prescription activity into the hands of the consumer/patient.

• Data collected from 38,000 sample pharmacies.

• ~260 million prescriptions collected each month

• More than 1.1 million prescribers representing 170 unique specialties

• Information collected from the Prescription Database creates Xponent and NPA.


The Sales Database

• Measures both national and sub national sales dollars, units and prices of pharmaceutical drugs purchased by Retail and Non-Retail pharmacies.

• Data collected from both manufacturers’ direct sales (~ 100 pharmaceutical companies) and indirect sales from over 500 distribution centers.

• 1.5 billion transactions are processed each year.

• Information collected from the Sales Database creates DDD and NSP.


Comparison of National DatabasesAudit Distinctions

Characteristic NSP NPA

Audit Definition National sales estimates of product packages into retail drugstores, hospitals, clinics, and other non-retail type outlets

National estimates of prescriptions, or the rate at which drugs move out of the pharmacy and into the hands of a consumer via formal dispensed prescriptions

Distribution Purchases (Supply) Dispensed Rx’s (Demand)

Source of the Data

Warehouse ship-to invoice data and manufacturer reported direct sales

Pharmacy terminal data

Channel Coverage

RetailMail (includes VA mail) LTC (includes VA Nursing Home)Clinics Federal Facilities HMOHome HealthcareMiscellaneousNon-Federal Hospital

Retail Mail (excludes VA mail)LTC (excludes VA Nursing Home)

Projections All channels projected, except Mail All channels projected

Data Availability Monthly Weekly and Monthly

Specialty No Physician Specialty Data Physician Specialty available

Reporting Timing 4-4-5 reporting Calendar reporting


National Sales Perspectives4-4-5 Calendar for 2009


Appropriate Use of IMS InformationApplying Tools and Resources

Available resources may explain variances

Method Recommended Usage

IMS Confidence Intervals

Confidence Intervals are built around the current estimate (i.e. +/- from the current estimate) and are good to use if the estimate needs to be compared to another number, such as a

“target,” to evaluate if two values are statistically

significantly different.

NPA Monthly Expected Change Tables

-Reflect the variations created by the number of days for each day of the week, for a given month, weighted by the relative importance of each day of the week.

-Major holidays are given their own unique weights. Provide

insight into expected month to month (year over year) trends.

Historic Variability -month to month/week to week variability

If attempting to validate a month-to-month (or week-to-week) change, historic variability will provide a range of potential values

IMS Product News Communications

May explain data anomalies or market events that are reflected in the data

Note: Typically one would not use all methods. Follow the general guidelines above on which tool is appropriate for each situation.


Confidence Intervals


Interpreting dataConfidence Intervals – What do they tell us?

• A Confidence Interval provides a range of values that are a “statistical tie”

−A value outside a Confidence Interval is unlikely to occur undertypical conditions

− It is said to be “statistically significantly different” from typically occurring values

−Provides “point in time” views of a data point

• Utilizing Confidence Intervals when using statistical estimates builds robustness into decisions

• IMS provides confidence interval tables for the national offerings


Understanding estimates based on a sample

• Building “tolerance” around estimates

• Confidence interval around estimate to account for sample to sample variation:

XTrue value(unknown)

Statistical Estimate(known)

X( )



Possible Outcomes

• If each estimate has a CI about it, then the difference is statistically significant if the two intervals DO NOT OVERLAP

( ) (( ))

• If each estimate has a CI about it, and one or both estimates fall inside interval of other then the difference is not statistically significant

( (( ) ))

• If each estimate has a CI about it, and intervals overlap but neither estimate falls inside the interval of the other then the result is inconclusive

( (( ) ))

Ŷ1 Ŷ2

Ŷ1 Ŷ2

Ŷ1 Ŷ2

1.

2.

3.

NPA Confidence IntervalsHow to apply NPA Confidence Intervals

1. Identify the appropriate Confidence Interval Table

a. Identify if the product in question is Branded or Generic, etc.

b. Use product volume for data point in question within a particular channel and locate the appropriate row (always default to lower volume)

c. Locate Channel in question (note that attached example pulls retail channel table)

d. Locate percentage for 2 standard deviations (95% confidence interval)

2. Calculate the upper and lower value and apply to target data point

a. Multiply 95% confidence interval to data point in question

b. Compare upper and lower bounds to target data point



Are the October 2008 and November 2008 TRx volumes statistically significantly different?

Confidence Intervals Example

= 617,054 + 2.62%

= 617,054 - 2.62%


647,555 614,489

= 631,022 + 2.62%

= 631,022 - 2.62%

CONCLUSION: After applying the upper and lower bounds, it can be concluded that the November 2008 TRx volume is a statistical tie to the prior month, October 2008, and therefore NOT statistically significantly different.


600,888 614,489 633,221 647,555

Nov. 617,054

Oct.631,022

Ŷ1Ŷ2

NPA Expected Change Tables


Interpreting DataNPA Expected Change Tables

• Removes the expected month-to-month changes to gauge a “true” percent change

• Updated to reflect the new calendar year

• NPA estimates take into consideration:

−number of days in each month

−differences in prescription volume processed through Retail, Mail and Long-Term Care pharmacies according to each day of the week

• NPA Expected Change can only be used for monthly data, not applicable for weekly trends.


Sample NPA Expected Change Table


NPA Expected Change TablesHow to apply NPA Expected Change

1. Identify appropriate Expected Change table

− Retail, Mail Service or Long-Term Care

2. Calculate month-over-month percent change

− (Current month – Previous month)/Previous month

3. Compare month-over-month observed percent change to month-over-month expected change


Expected Change Example

• What is the “true” fluctuation in NPA Retail volume from January 2008 to February 2008 if you back out the variance due to the number of days in the month and differences in volume processed by each day of the week?


CONCLUSION: After applying the expected change to the observed growth, the smoothed change for Product A is -4.29%.

Instructions1.Calculate % change over preceding Month.

2.Identify observed % change and subtract the “expected” % change from the chart.

Example: [(-9.39) – (-5.10) = -4.29%]

3. Result is a “smoothed” % change which eliminates the effect of changing the distribution of weekdays.


Historic Variability


Interpreting dataWhat is a historic variability trend?

• Provides a range of potential values when attempting to validate month-to-month or week-to-week changes.

• Historic variability creates tolerance bands that account for the natural variation over time.


Interpreting the data “Normal variations” exist in trends over time

• Quantify historic month-to-month % variation; and use to distinguish if current period change is reflective of normal trend

Month to Month Percent Change in TRx Volume: Aug 06 to Jul 08

(Product A Oral Solid)

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

Aug-06 Sep-06 Oct-06 Nov -06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May -07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov -07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May -08 Jun-08 Jul-08

% C

hange in T

Rxs


Apply ResourcesCreating month-to-month historic variability in Excel

Use Average and Standard Deviation (two Excel built-in functions in functions list) and create your upper and lower bounds for acceptable variability in the month-to-month change.

All the computations are listed here.

Column A: Sort months in ascending order

Column B: TRx Volume

Column C: Calculate % change (see formula)


Historic Variability Example

• Is the published IMS TRx growth estimate for the September 2008 data month consistent with the historic variability for this product?


Apply ResourcesHow to calculate and apply Historic Variability

1. Calculate month over month or week over week percentage change: (Current time period –Previous time period)/Previous time period

2. Calculate average percentage change based on 24 historical time periods, not including the period in question [AVERAGE formula]

3. Calculate standard deviation [STDEV formula]

4. Calculate upper and lower variability tolerance based on 2 standard deviations [Average +/- (2*stdev)]

5. Compare upper and lower variability tolerance to percent change


CONCLUSION: The variance is within acceptable historic variability for the period (i.e., -8.14% is between -9.2% and 15.7%).


Interpreting dataExamining Historic Variability

• Use for any estimate tracked over time: volumes, shares, dollars, quantities (extended units)

• Use for tracking time periods: monthly, weekly, daily (examine by day of week),

• Use for tracking products: Generics, Brands, or USC Therapeutic Categories, etc.

• Remove obvious differences among time periods prior to creating bounds

• Use with at least 24 time periods and two (2) Standard Deviations

• Our recommendations for tighter bounds:

• Trend % change as opposed to volume when tracking new products (get closer to normal approximation)

• Apply expected change tables when tracking monthly Rx volumes of drugs in NPA (accounts for shorter months, etc.)

• For monthly NSP dollars and extended units, divide by number of weeks in month and apply bounds to average weekly numbers (to account for 4-4-5 calendar month)


IMS Communications


Review IMS Communications

If, after applying the resource applications, questions still exist, review IMS Product News:

• Market event

• Enhancement

• Advisory

• Follow-up

If applicable, apply guidance, and analyze impact reporting results


Interpreting IMS National Estimates

Step 1:

Data is published

Step 2:

Apply Resources

Step 4:

Contact IMS

Receive published estimate

Interpret data in light of market & company knowledge

Calculate Confidence Intervals

Apply NPA Expected Change Tables, if applicable

Analyze data in light of Historic Variability

Step 3:

IMS Communication

If questions exist:

Review published Product News communications

Apply Guidance and analyze impact reporting results, if applicable

If questions exist:

Contact your IMS Account representative for initiation of a data investigation


Applying Tools and ResourcesClosing summary

• A true picture of the market is extremely intricate and multi-faceted due to multiple sources of variation

• IMS is well positioned to handle the market intricacies and variation

−IMS captures a robust and broad sample

−Utilizes our patented estimation method

−Employs data collection and communication best practices

• In conjunction with IMS, the Financial Community can employ practices that:

−Distinguish normal variation from true performance indicators byusing tolerance bands

−Set expectations that volume fluctuations, regardless of root cause, that fall within normal expected bands do not represent statistical differences


Guidelines for National Database ComparisonsApplying Best Practices

Guidelines for Comparing AuditsMarket Definitions

• When comparing sales and prescription data, always ensure that market definitions, channel definitions, product/product groupings, forms, or strengths match across IMS databases.


Guidelines for Comparing AuditsMetrics

• Dollars are not the recommended measurement to use when comparing sales and prescription data.

Price Amount Source

Pharmacy Retail

Price

$2.20 NPA

Pharmacy

(Transaction)

Acquisition Price

$1.65 NSP Retail

Wholesaler

Acquisition Cost

(WAC)

$1.75 DDD

Example Price per Extended Unit:

Product “A” 10mg Tablet


Guidelines for Comparing AuditsMetrics, continued

• Prescriptions are NOT the recommended measure to use when comparing sales and prescription data

• Extended Units ARE the recommended measure when comparing sales and prescription activity

−Prescription Extended Units generally correlate to NSP Extended Units

• Extended Units may vary depending upon pharmacy reporting practices


Example of Data Measure Distinctions NSP and NPA: Impact of Extended Unit (EU) definitions

Source: NSP Extended Units (EU RET) and NPA Extended Unit Total Dispensed Prescriptions (EUTRx)

Pack configuration:12 tubes of .25 grams

• NSP Extended Units= # of grams

• NPA Extended Units= # of tubes

Once NPA is factored to represent grams, NSP and NPA show similar volume trends


Guidelines for Comparing AuditsTime Periods

• A three-month rolling average is the recommended time period to use

Formula:

(3 months summed) divided by 3 = three-month rolling average

• (January + February + March) / 3

• (February + March + April) / 3

• (March + April + May) / 3


Guidelines for Comparing AuditsChannels

• Traditional Retail Channels are suggested for cross-audit comparisons

−Traditional Retail Channels (Chain, Independent, Foodstore pharmacies) are reported across both the sales and prescription databases and are by definition, similar.

− Long-Term Care Channels are reported across both the sales and prescription databases and are by definition, similar.

−Mail Service definitions across databases are dissimilar.

• Projections

• VA Mail


Case StudyComparing data from two audits: Dollars vs. Dispensed Rxs

Source: NSP Dollars (DOL TOT) and NPA Total Dispensed Prescriptions (TRx)

Why are NPA and NSP volumes and trends NOT SIMILAR?


Applying Guidelines

Step 1:

Compare

Step 2:

Metrics

Step 4:

Channels

� Identify key audit distinctions

� Market Definitions

� Product/Product Groupings

� Form and Strength

� Use Extended Units

� Ensure Extended Unit definitions across databases are the same

Step 3:

Time Periods

� Recommend three month rolling average

� Compare Channels�Retail:ChainIndependentFoodstores

�LTC

� Mail dissimilar�Projections

�VA Mail differences


Applying GuidelinesStep 1

Step 1:

Compare

Step 2:

Metrics

Step 4:

Channels/ Subcats





Ste 3:

Time Periods

NSP NPA

Dollars Prescriptions

4-4-5 Schedule Calendar Month

Retail, Mail, LTC & Non-Retail

Retail, Mail, LTC

VA Mail Included VA Mail Excluded

Unprojected Mail Projected Mail


Applying GuidelinesSteps 2 and 3

Step 2:

Metrics



Step 3:

Time Periods

� Recommend three-month rolling average


Case StudyComparing data from two audits

NSP reporting is 4-4-5 vs. NPA calendar reporting.

NSP Dollars

NPA TRXs

Source: NSP Dollars (DOL TOT) and NPA Total Dispensed Prescriptions (TRx)


Apply Appropriate Use Metrics and Time PeriodsExtended Units and Average Rolling Months applied

Source: NSP Extended Units (EU TOT) and NPA Extended Unit Total Dispensed Prescriptions (EUTRx)


Applying GuidelinesStep 4

Step 1:

Compare

Step 2:

Metrics

Step 4:

Channels

Step 3:

Time Periods


�LTC




Apply Appropriate Use ChannelsLike Channels applied (Chain, Independent, Foodstore, Mail & LTC)



Further Break-out of Like ChannelsRetail Channel view (Chain, Independent and Foodstore)



Applying Guidelines

Step 1:

Compare

Step 2:

Metrics

Step 4:

Channels







Step 3:

Time Periods

� Recommend three month rolling average


�LTC




NPA Rx Measures Marketplace Dynamics of Pharmacy Practices

IMS definition of New, Refill and Total Rx

• NRx

−Prescriptions issued by the pharmacy with a unique prescription number

−A new prescription may or may not represent new therapy for the patient receiving the prescription

• Refill

−Prescriptions issued under the same prescription number as an original (new) prescription previously recorded by the reporting pharmacy. Refill prescriptions are necessary for seeing the total prescription volume for a product or market

• TRx

−New prescriptions plus refill prescriptions reported by pharmacies.


Standard pharmacy “New Rx” designation practice

Patient presents new prescription at pharmacy with refills

Prescriber writes a new Rx

Pharmacist

•Inputs required adjudication and tracking information

Pharmacy System

•Generates a unique Rx number and flags that as ‘New Rx”•Most Pharmacies track all subsequent Refill Rx identified with the same prescription number


Some pharmacy business processes may cause variation in NRx measures that may not be indicative of a new prescription being presented at the pharmacy

1. Pharmacy assigns a new prescription number to a “refill” when dispensed NDC number changes. This may occur when:

� Pharmacy changes the source of a generic or brand product (repackager) resulting in a new dispensed NDC

� Pharmacy stocks a different package size than traditionally stocked, resulting in a different NDC

� Patient may switch from brand to generic, or vice-versa

� Managed Care guidelines may require a New Rx yearly, or twice per year

2. Retail, Mail or LTC Supplier changes dispensing location, resulting in an assignment of a new prescription number

Examples

However some exceptions do exist


Common Drivers of Pharmacy PracticesInfluences on New to Refill Ratios

• Drivers of this practice may include:

−Supply shortage of a specific manufacturer’s generic product; in some cases this will result in the pharmacy sourcing the product from another manufacturer

−New pharmacy product package size introduced into the market

−A mail service pharmacy reallocates workload by transferring scripts to satellite pharmacies

• What to anticipate:

−A temporary increase in NRx measures with no corresponding increase in TRx measures


Recommendations

• To the degree you are aware of this type of market event, use prior or subsequent NRx/RRx ratios

− Use the average of the most recent 6-8 non-impacted weeks, prior to any NRx market event occurring

− Exclude holiday weeks, in case there are unusual distribution during these time periods

• IMS continues to work with our data suppliers to mitigate the impact of these sorts of unusual data handling practices.

• IMS longitudinal prescription information will assist in minimizing Retail supplier based changes in NRx.

− Longitudinal prescription services categorize prescriptions using anonymous patient IDs, measuring New to Brand (NBRx) or New Therapy Start prescriptions.

− These services offer alternatives that alleviate NRx fluctuations and enable greater insight into drivers of TRx performance


Other IMS Offerings

�New To Brand�Portfolio Profiler�NSP and NPA Pricing Measures Database

NPA New to BrandMeasuring true new prescription activity

• A national key performance indicator that integrates industry-leading NPA with de-identified patient-level data

• Provides enhanced visibility to the volume of true, first-time prescriptions on a brand

• Only longitudinally-derived metric offered across all products and markets

• Accessible through investigator.web™ and Dataview™

• Additional, more granular insight provides a lead indicator of a brand’s ability to capture true new business


The full impact of the Avandia safety news played-out in two weeks through the New-to-Brand metric, while the impact as measured by NRx and TRx is still playing-out

Avandia Share

0%

3%

6%

9%

12%

Jul 2

8 06

Aug

18

06S

ep 0

8 06

Sep

29

06O

ct 2

0 06

Nov

10

06D

ec 1

06

Dec

22

06Ja

n 12

07

Feb 2

07

Feb 2

3 07

Mar

ch 1

6 07

Apr

il 06

07

Apr

il 27

07

May

18

07Ju

ne 8

07

June

29

07Ju

ly 2

0 07

Aug

10

07A

ug 3

1 07

Sep

21

07O

ct 1

2 07

Nov

2 0

7N

ov 2

3 07

Dec

14

07Ja

n 04

08

Total Rx New Rx New to Brand

TRx share loss 2.96% pts NRx share loss 3.8% ptsNBRx share loss 5.4% pts

The NBRx share loss represents 8,073 NBRx prescriptions per week

Case Example


IMS MIDAS Portfolio Profiler (Launching in December 2009)


Tracks Global Company Performance, Market Trends, and enables users to project potential merger impact

•Global IMS MIDAS Data available at 4 levels & 3 measures– Corporation/ Country/ International-Product/ ATC3 Therapeutic Category

– USD (US Dollars)/ LCD (Local Currency Dollars)/ SU (Standard Units)

•Breakout coverage of 15 markets– Core: EU5 (Top 5 European countries); US, Canada & Japan

– Pharmerging: BRICK TM (Brazil, Russia, India, China, Korea; Turkey & Mexico)

– ROW - Rest of World bucket (Quarterly Only)

•Data updated monthly where available & for all countries quarterly

– Monthly data points will be updated weekly until all monthly panels are live

•Hosted and secured on the IMS Customer Portal

•Delivery – Online Graph, Grid and Data Manipulation

– Export to Excel, PowerPoint and PDF files


Dashboard View


Country View (filtered)


Data Grid View

NPA Pricing Measures Database

The National Prescription Audit™ has key insights into actual Retail/Consumer pricing.

•Using Retail Costs derived from pharmacy terminals, NPA provides a valuable reference point for comparing Retail Costs per Rx generated by products within a competitive mix.

− Delivered as a Dataview™ database via File Express through the IMS Customer Portal

− Includes all markets

− Most recent three years of pricing history

− Available as a Monthly, or Quarterly Dataview database subscription


NPA Pricing Measures - Sample Report


Summary

−Identified key distinctions across the National Sales Perspectives and the National Prescription Audit databases

− Introduced and applied new tools & resources

• Confidence Intervals

• Expected Change

• Historic Variability trend

−Reviewed and applied guidelines for database comparisons

−Highlighted marketplace dynamics of pharmacy practices

−Outlined some additional IMS offerings to support Financial Community customers in the use of IMS data


Questions?


Cathy JeffersonService ManagerTel. (610) [email protected]

Kathy Constable Senior Client Service AnalystTel. (610) 832-5879 [email protected]

IMS Client Service Team:

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