COX-2 Concomitancy Analysis Jan 2, 05

CONFIDENTIAL
COX-2 Concomitancy Analysis 1
“The significant problems we face cannot be solved by the
same level of thinking that created them”
Albert Einstein
Intercon Systems Inc. 1155 Phoenixville Pike, Suite 103, West Chester, PA 19380
Phone: 610-516-1622 Fax 610 719-0414
COX-2 Concomitancy
Analysis

Contents
Introduction...................................................................................................................................... 1
Overview Of COX-2 Patient Population .......................................................................................... 2
Preparing For Analysis................................................................................................................. 4
Segmenting The Patient Population ............................................................................................ 8
Patient Viewer............................................................................................................................ 18
Longitudinal Concomitancy Analysis............................................................................................. 20
Analysis Issues To Be Considered ............................................................................................ 21
Concomitancy With Other Control Classes ............................................................................... 32
Ace Inhibitors.......................................................................................................................... 32
Calcium Blockers.................................................................................................................... 33
Diuretics, other non-inj. .......................................................................................................... 33
Angio II antag, alone .............................................................................................................. 34
Nitrites, Nitrates...................................................................................................................... 34
Anticoagulants........................................................................................................................ 35
Anti-platelets........................................................................................................................... 35
Alpha Blockers, alone, combination....................................................................................... 36
Anti-arrhythmia agent............................................................................................................. 37
Patients With More Than 6 Vioxx And/Or Celebrex Pickups..................................................... 37
Analyzed and reported by Aviel Shatz, CTO, Intercon Systems, Inc.
(c) Copyright by Intercon Systems, Inc. 2005
COX-2 Concomitancy Analysis

LRx Demonstration Analysis
Introduction
The purpose of this document is to provide insight into the analytic capabilities that come with the
IMS LRx Patient longitudinal data. The focus of this demonstration analysis will be on the
concomitant use of COX-2 inhibitors with cardiovascular drugs.
IMS provides access to patient longitudinal data at 2 levels:
1. Patient-level, individual de-identified patients with script information detailed down to
NDC.
2. Summary-level, from zip code up to national views.
This document will focus on patient-level data. As you work with the tools that come with the IMS’
longitudinal patient data, there are a few important concepts to keep in mind.
1. You have access to and can export or analyze patient data at its lowest level of detail.
There is no loss of granularity when the data is loaded into the FDA Patient LDM
(longitudinal data mart).
2. You can mine data at this level of detail and the response time is measured in seconds,
regardless of the number of patients in the database. The speed is achieved thru
specialized relational database enhancements that are unique to IMS in the healthcare
market.
3. From the master patient longitudinal data mart (LDM), other LDM’s containing a subset of
patients that fit specific analytic requirements can be extracted. These subset LDM’s are
updated with the same frequency as the master LDM.
4. New variables and patients classifications can be defined using the raw data elements in
a Patient LDM. These are also automatically updated when the Master LDM is updated.

Overview Of COX-2 Patient Population
Analysis begins by reviewing the Master Patient Longitudinal Database.
Opening the dynaTrack application presents a list of all the patients in the master patient file.
Click to open data dictionary and
select fields for report layout
Patient count
An epidemiological analysis begins by extracting an actionable, relevant epidemiological
longitudinal
database.
Fields selected for report layout
Field titles and record
content for selected table
Menu of tables in
the Patient LDM
Clicking on the
Report Layout
icon displays a
data dictionary.
Report layout is
designed by the
analyst and
saved.

The concomitancy analysis process begins by segmenting the population by Therapeutic class.
Click bar for menu of categories
The ‘Antiarthritics’ is selected from the menu of therapeutic
classes. Patients in this class will be the ‘Target’ of our sample
analysis.
Selection precision can be adjusted before segmentation is done,
or a two stage ‘extract’ process can be run, the first one ‘extract’ a
rough selection. The second ‘extract’ results in a precise
selection.

Drug hierarchy definition tool
Array of classes each patient was
exposed to, in addition to Antiarthritics.
25.6 Million patients who received Antiarthritic drugs were selected. The ‘Drug (base level)’
column lists the array of Classes each patient was exposed to, concomitantly with Antiarthritic
products, during the study period.
For our analysis, we have chosen to select all the patients within the Antiarthritics Class exposed
to Vioxx, Celebrex or Bextra.
The patient LDM is ‘Exported’ from the Master LDM.
Preparing For Analysis
The patient LDM is conditioned for analysis in 2 steps:
1. Defining a product hierarchy by selecting products to be included/excluded and by setting
data summarization levels for the analysis.
2. Creating variables and segmentation ‘properties’ (akin to dimensions in a data cube).

By opening the Drug Hierarchy
dialog, analyst selects the
product classes and subclasses
to be analyzed for concomitancy.
Prescription data is detailed in
the database by NDC code. With
the Drug Hierarchy dialog,
analyst selects the ‘study’
products to be analyzed and the
‘Control’ products that may
provide insight on efficacy and
risks for ‘study’ products.
the
n.
e
ss are
) level.
The Drug Hierarchy dialog
discards all products associated
with ‘black’ coded classes.
Classes, subclasses or products
can be set for the desired level
of accumulatio
Black boxes identify
excluded classes
Green boxes identify classes for
inclusion in ‘Drug (base level)’
For our demonstration analytics project,
we have selected for ‘Control’ products
the range of drugs associated with CV
events and strokes.
In the view opposite, all NDC codes
classified as ‘Anti-arrhythmia agents’ are
represented as ‘Anti-arrhythmia agents’.
The same depth of accumulation setting
is set for ‘Nitirites & nitrates’ within th
‘Coronary vasodilators’ class. Drugs
within the ‘Cardiac Agents, oth’ cla
accumulated at the individual drug
(brand

Study products are marked for
accumulation and review at the
brand level.
The current selection is saved.
Analyst can define alternative
‘market definitions’ and apply
each definition within the
framework of required analysis.

The second ‘preparatory’ process is to provide analytical overview for the patient population in the
current database.
Properties categorize and scale each patient in the database by attributes that are driven by
prescription history data. For example, the ‘Sum Vioxx, Celebrex, Bextra’ property (Selected in
the ‘list of Defined Properties) classifies the patients into 10 groups, set by the sum of
prescriptions for each patient dispensed over the two year data period.
In similar process, patients can be ‘classified’ by the number of ‘Anticoagulants’, ‘Anti-arrhythmia
agents’ and other products, as specified in the currently selected ‘Product Hierarchy’ setting,
consumed over the two years period.
A ‘calculation’ process follows the property definition step. The entire database of patients is
processed. Each patient is analyzed and categorized by each property attribute.

Segmenting The Patient Population
Once properties are
calculated, Analyst can
display the list of properties
and select a property for
presentation. The currently
selected property is
‘Sum_Vioxx, Celebrex,
Bextra’.
The graph below presents
the distribution of the 8.1
Million patients by the
number of scripts consume
in the analyzed period
63
d
(2 yr).
• 3.924 Million patients
were exposed to a single
script in the period.
• 297,552 patients were
exposed to 17 and more
pickups in the period.
Patients with 17 or more
Vioxx, Celebrex, or
Bextra script pickups
over a 24-month period
Patients with a single script
for Vioxx, Celebrex, or Bextra
‘Sum Vioxx, Celebrex,
Bextra’ selected for
Axis-X

For AxisY, the ‘Sum of Ace inhibitors, alone’ class was selected. From the 8.163 Million patients
exposed to COX-2 products, 184.6K were also exposed to Ace Inhibitors. The yellow tip splits the
12,317 patients subject to 17 and more refills on COX-2 to the number of scripts exposed to from
the Ace inhibitors class.
The graph identifies an increase in concomitancy level, associated with chronic use of COX 2
inhibitors drugs.

Concomitancy summary for COX 2 products with Cardiac Agents:
Overall
Concomitancy
in K
Concomitancy
On High users
in K
High users of
COX-2
concomitancy
with High on
Cardiac agent % of High ratio
% of High COX-
2 users on High
on Cardiac
agent
663 18 6.4 2.71 35.06
Systematic Analysis on all therapeutic classes can be summarized by the following table
(insignificant concomitant classes were omitted):
Overall
Overlap
in K
patients
Concomitancy
of High users
in K
High COX-2
concomitacy
with High on
Class
% of
High
ratio
% of High
Cox-2 users
on High on
Class
Anti-infective 4,846 84 9.7 1.73 11.5
Analgesics 4,656 77 22 1.65 28.6
Psychotherapeutics 3,133 61 26 1.95 42.6
Vascular agents 3,027 75 28.6 2.48 38.1
Hormones 2,956 59 15 2.00 25.4
Musculoskeletal 2,497 47 18 1.88 38.3
Gastrointestinal 2,457 57 30 2.32 52.6
Cough/cold 2,188 40 4 1.83 10.0
Antihyperlipidemic 1,841 48.9 26.6 2.66 54.4
Respiratory 1,824 39.9 9 2.19 22.6
Diuretics 1,640 45 20 2.74 44.4
Antifungal 1,323 27 2 2.04 7.4
Ophthalmic 1,086 25 3.7 2.30 14.8
Dermatologicals 1,027 20 1.3 1.95 6.5
Antinauseant 943 18 1.8 1.91 10.0
Sedatives 939 18 5 1.92 27.8
Genitourinary 823 19 6 2.31 31.6
Diabets 823 20 7.5 2.43 37.5
Thyroid 781 19.7 10.7 2.52 54.3
Hemostatics 670 18 7 2.69 38.9
Nutrients 666 19 7.7 2.85 40.5
Cardiac agents 663 18 6.4 2.71 35.6
Laxatives 623 15 1.8 2.41 12.0

Overall
Overlap
in K
patients
Concomitancy
of High users
in K
High COX-2
concomitacy
with High on
Class
% of
High
ratio
% of High
Cox-2 users
on High on
Class
Blood growth 462 12 4 2.60 33.3
Antiviral 445 8 0.7 1.80 8.8
Contraceptives 386 1.9 0.7 0.49 36.8
Diagnostics 384 11 2 2.86 18.2
Antineoplastic 270 8.4 3.5 3.11 41.7
Antispetics 194 4 0.3 2.06 7.5
Antidiarrheals 176 4.5 0.48 2.56 10.7
Anti obesity 145 2.5 0.48 1.72 19.2
Antimalarials 101 3.7 1.9 3.66 51.4
Anti arthritis 72 1.9 2.64 0.0
Antiacids 45 1 0.2 2.22 20.0
The high degree of concomitancy between COX-2 products and other therapies hinders the
possibility of managing properly controlled Clinical Research projects.

Returning to the analysis on ‘Sum of Ace inhibition alone’ class, analysis will be provided on
patient’s age as confounding property. For simplicity of discussion, two categories of patients, at
the age group of 30-39 and 50-59 are selected.
The differences are striking.

Age is associated with an increase in concomitancy between COX-2 drugs and ‘Ace inhibitors
alone’ drugs.
Gender seems to be an important confounding variable, when observed by absolute numbers.

However, from depth of concomitancy, males and females present about the same
characteristics.
A quick view of gender breakdown reveals that females are 62% of COX 2 inhibitor drugs.

Aggregated Scripts: The graph and table below summarizes the number of scripts, for the target
and concomitant drugs, over the period. This summarizes that risk associated with COX-2 drugs
cannot be assessed without researching the effect of the concomitant drugs.
Number of patients in
pickup aggregation
The table quantifies the number of scripts picked monthly by the COX-2 patient population.

The graph and yellow tip below summarize the aggregated number of scripts picked in April 2004,
with CV related concomitant drugs. In this month, 318,718 scripts were picked; only 85,191
scripts were for the COX-2 drugs.
Accumulation for the period details the distribution of scripts.
Highlighted products are
removed from report
Accumulated
scrip pickups for
COX 2 products

Patient Viewer
Until the current analysis phase, patient data was analyzed with no accounting for the longitudinal
(therapy time) dimension. Patients and scripts were viewed over time based on calendar periods.
The balance of the analysis will look at Patients and scripts aligned to day one of therapy. Time is
expressed in therapy days.
The process demonstrated hereunder is run on dynaMed, Intercon Systems Inc.’ s
Epidemiological Analysis system.
dynaMed is engineered to store, display and analyze a variety of data types (prescriptions,
medical diagnosis and procedures) longitudinally. The present analysis relied entirely on
prescription data. The reference to other types of data is made only to describe dynaMed’s data
management and analytic capabilities.
Therapy
time scale
Graphic presentation of
specified event types
for selected Patient
Events/activities
associated with a
single Patient
Patient list
The patient viewer above presents data for a selected patient, longitudinally. The Patient Viewer
is divided into 3 parts:
1. (Top) A patient list for choosing individual patients.
2. (Middle) A patient’s record provides all event/activity details for a chosen patient.
Prescriptions are listed along with medical services details, diagnostics, hospitalization
and other events.
3. (Bottom) Events are presented graphically, on top of the X time horizontal axis.
Geometric shapes are associated with event type by the user. The symbol is painted for
the event’s specifics attributes (product class, treatment type etc.).

Within dynaMed, diagnostic
codes are identified and
selected, facilitating selection of
target populations for analysis,
by diagnostic codes.
Below is a view of a typical patient receiving concomitant drug therapy. The middle table lists the
picked drugs. The color-coded graph at the bottom presents the target and concomitant drugs
over time on the X axis. Each bar starts on the date the script is pickup and ends on the
anticipated refill date, according to Days of Therapy on the script. This patient is taking Celebrex
and a Proton Pump inhibitor concomitantly.
Celebrex script
Proton pump inhib. script

The next example (below) presents a patient whose therapy was switched from Vioxx to Bextra.
Analysts can segment the patient population before analysis, review the pickup and
concomitancy patterns, to gain insight on disease state evolution.
Longitudinal Concomitancy Analysis
Concomitancy analysis summarizes the progression of concomitancy between each of the ‘Study’
products and their ‘Control’, as they evolve over time. There are 3 concomitancy scenarios to
consider in this analytical example:
1. When a COX-2 inhibitor drug that is strongly associated with CV events, such as Nitrates
or anti-coagulants picked before the first COX-2 inhibitor drug, evidently the COX-2 drug
cannot be a suspect for ‘causing’ the CV event.
2. When a COX 2 drug is prescribed concomitantly with the CV related drug, it can be
assume that the COX 2 was prescribed to care for pain, potentially caused by a CV
event.
3. When CV related drug is initially picked while COX 2 drug is being used, or following
COX 2 product discontinuation, potential causal relationship hypothesis can be
investigated.
The dynaMed analytical engine accounts for each patient’s longitudinal drug pickup detailed
records categorizing, measuring and summarizing the relation between the first pickup of ‘Target’
product and the first ‘Control’ product. If there are multiple ‘Control’ products (or product groups),
the measurement is performed for each product separately.

Analysis Issues To Be Considered
1. Target (study) and Control prescriptions, picked on the initial data period cannot be positively
classified for their timing of concomitancy. If the record is on study product picked on the first
data month, it will be uncertain if the patient was ‘new to therapy’, or a ‘repeat’ patient. To
accommodate for that ambiguity, a ‘lead-in’ period can be optionally specified. Patients who
picked a study product within the ‘Lead-in’ period are dropped from the analytical summary.
2. When patient’s first pickup of a ‘Target product’ is close to the last day of data, they increase
counts on concomitancy that happened prior to their first pickup of study drugs, but will not
account equally to the concomitancy that follows that first pickup. To reduce that bias, a
‘lead-off’ period is optionally set. Patients who picked their initial study product within the
‘Lead-off’ period and did not pick a Control product earlier, are dropped from the analytical
summary.
3. Statistics on ‘concomitant’ products consumed prior to first ‘target’ product may be inflated, as
prescriptions for the first months are either ‘new’ or ‘continuing’ therapies. When a ‘lead-in’
period is specified for Control products, patients who picked a Control product within the
‘Lead-in’ period are dropped from the analytical summary.
4. As the number of patients who picked each of the ‘study’ products varies, comparison of
numbers for the ‘study’ product’s concomitancy with ‘control’ is facilitated after accumulated
statistics are ‘normalized’. Normalization methodology resizes all statistics to the population
of the lead product that is selected as ‘normalizing’ product in the setup dialog. Alternative
method normalizes all measures by a common standard, such as: ‘per million patients’, or
‘per 100,000 patients’. Standardizing population is set on the ‘Proportion’ data box.
Clicking the [Run] button starts an analytical process, reviewing and analyzing each patient
record, categorizing the measures by products and accumulating the results in memory. At any
point in time, analysis process can be stopped, interim results can be reviewed and analysis
resumed.

When the [Report] button is clicked, the ‘Overview of concomitancy progression over time’
screen is displayed.
Overview window has 3 distinct areas.
1. The three tables on top control the products in the report and provide statistics on the number
of patients for each Target and Control product.
2. The horizontal graphic selector bar in the center provides statistics and selection of patients,
by their number of pickups of Target drugs.
3. The main (bottom) graph summarizes the concomitancy progression over time, for each
product class, for each Target product. Clicking on the graph maximizes it. Click on each
Control product label opens a detailed report on concomitancy on the clicked drug.

The Target Products table lists the number of patients in the database exposed to the target
product. In the example above,
2,069,415 patient were exposed to
Vioxx over the two year period.
From that patient population, 21,028
picked a script for more than a
single Target product. 2,048,387
were on Monotherapy with Vioxx.
1,644,766 patients are listed as
Analyzable – that they were
exposed to one or more drugs listed
in the ‘Control’ list.
Clicking on the column heading will
sort the list, by drug name or by the
number of concomitant patients.
When the ‘configuration’ of target
products is modified, the details for
the ‘Control’ products are modified
accordingly. The report opposite
lists the Control products after
‘Bextra’ was joined into the
analysis. Number of patients
subject to Beta Blockers was
modified from 494,098 to 648,020.

The ‘Concomitancy analysis’ graph lists the currently selected ‘Control’ drugs on the X axis, each
‘Control’ is detailed by the currently selected ‘Study’ products. The graph is sorted, from left to
right following the sorting of the ‘Control drugs’ table.
Clicking on the body of the graph will maximize its view. Clicking on a class label opens a detailed
report for the class.
Report settings: DataView = Normalized, GraphView = Comparative, DistributionView =
Standardized
The ‘Concomitancy analysis for ‘Beta blockers’’ reports on the evolution of concomitancy
between Vioxx, Celebrex and the first instance any ‘beta blocker’ drug has been picked by a
patient.
1
st
Vioxx Script
(# of patients)
1
st
Celebrex Script
(# of patients)
The first day on Month 0 is the day when the ‘target product’ (Vioxx or Celebrex, under the
current setting) has been picked. The two bars account for the number of patients who picked
their first script, a Study product and Control product.

The bars for Month-1 represent the number of
patients, who picked their first Control product within
30 days before the day picking the first ‘Study’
product. The colored bar details the patients by the
‘Study’ product.
Pointing with the mouse over the X axis label pops up
a yellow tip. 32.8 patients have picked Beta blocker a
month before picking the first script for Vioxx. 40.1
picked Celebrex on the same relative period.
The detail tip for Month-21 (600 to 630 days before
picking the first Study product), 40.9 Vioxx patients
picked their first ‘Beta blocker’. A total of 779.1 Vioxx
patients picked their first ‘Beta blocker’ prior to picking
a Study product. That number compares to 904.5
Celebrex patients.
The current graph presents ‘normalized’ numbers, adjusting the sets of detailed numbers to the
different investigated population size. Discussion of normalization process will follow. It suffices to
note that ‘normalization’ turns the absolute accumulated counts into comparable figures.
Putting the mouse pointer on a bar displays the formula as well as the intermediate values used
to calculate the value represented by the bar.
In the example above, 11 months before picking their first Celebrex script, 613.5 patients picked a
Beta blocker. The calculation is 613.5/1,683,084 per single Celebrex patient, or 36.5 pickups for a
‘standard population’ of 100,000 Celebrex patients.

With the ‘Cumulative lines’ removed, the two
regression lines indicate the ‘normal’ entry into
therapy by ‘Beta blocker’ products for patients
who will be picked by a physician to rely on
either Vioxx or Celebrex. The elevated
numbers for month -20 and -19 could be
attributed to the mis-categorizing of continuing
patients as ‘new’ (a higher selection of ‘lead-in
months’ for Control products may correct that
phenomena, while dropping more patients
from analysis.
t pickup.
The graph represents that Celebrex is more
strongly associated with the use of Beta
blocker drugs, compared to Vioxx, prior to the
first pickup of either product.
Another phenomena that is worth noting, from
month -10 and onward, pickup of ‘Beta
blocker’ drugs is accelerating towards the
event of Study produc
The report for the post-period
demonstrates a clear lower level of entry
to Beta blocker regimen for patients who
were exposed to either Vioxx or
Celebrex. The ‘hatched’ segment of the
bars represent the proportion of patients
who were exposed to refills of Study
products while picking up the Beta
blocker product.

The numbers are summarized on the following table.
Report Settings: DataView = Absolute, GraphView Comparative, DistributionView =
Absolute
To follow the analytics methodology, the setting options are rest to ‘Data view’ of ‘Absolute’
numbers, and ‘Distribution view’ is also set as ‘Absolute’. The Overview graph is redrawn.

The report above summarizes the raw data, as accumulated while reviewing the database.
As per the setting on the ‘Setup’ dialog, patients were
excluded from the accumulation if they picked a Beta-blocker
(Control Product) during the first 60 days of data period.
Patients who picked a study (Target) product during the first
or last 90 days of the data period were also dropped.

The total number of patients who picked a Beta-blocker (control product) after picking a Study
product was 88,685 for Vioxx and 83,916 for Celebrex. This can be compared to 188,214 Vioxx
patients and 181,605 Celebrex patients who picked a Beta blocker before picking their study
product.
On ‘Month 0’, 19,354 Vioxx
patients and 21,756 Celebrex
patients picked Beta blockers
simultaneously. 4,682 Vioxx
patients and 4,144 Celebrex
patients picked their first Beta-
blockers within 30 days from
Study product pickup.

While the accumulated numbers are precise and verifiable, the presentation of the data is
improper. The ‘virtual’ month 0 represents accumulation of data from 21 ‘real’ months of data (24
month less three ‘lead-off’ months’).
The number accumulated for ‘month -1’ is accumulated for couple of months, the month of the
first Study product pickup and the month of the first ‘Control’ products. There are only 20 such
pairs.
‘Month -21’ represents data about patients who picked their Study product in the last active data
month (month 24 minus 3), who picked their first Control product 21 months earlier. There is only
a single such pair.
By ‘Normalizing’ the accumulations, the accumulated numbers are divided by the number of
instances represented.
The ‘Normalized’ view is quite different. Note that the number of patients for the Study products
on month-21 is the same as before normalization, as data was divided by 1.

The ‘Normalized’ numbers for
Month 0 (24,036) were divided by
22 (1,092.5).
The resulting presentation presents the progression of concomitancy over time, however, it is not
ready for proper comparison between study products, as it does not account for the different size
of patients exposed to the various Study products.
The ‘Standardized’ view resets the count for the multiple Study products, by the number of
‘Normalizing product’ population. As in our analysis Vioxx was selected as ‘Normalizing Product’,
the following report is generated:
The overall phenomenon identified has not changed, but the differences in physician’s induced
differences are more pronounced.

A detailed tip is generated when the mouse is pointing on graphical bar ‘Month-2’.
Concomitancy With Other Control Classes
To complete the presentation, hereunder are reports on CV and stroke related concomitant
therapies.
Ace Inhibitors

Calcium Blockers
Diuretics, other non-inj.

Angio II antag, alone
Nitrites, Nitrates

Anticoagulants
Anti-platelets

Ace Inhibitors, other
Alpha Blockers, alone, combination

Anti-arrhythmia agent
Patients With More Than 6 Vioxx And/Or Celebrex Pickups
A hypothesis that may be of interest to investigate is the relation between the number of pickups
of study drugs, and their effect on the pickups of ‘Control’ products.
The patient selector segments the patient population by the number of target refills. Clicking on
the graph ‘selects’ patients inclusion in the analysis reported by their number of pickups of Study
products. By selecting of only patients with more than 6 pickups of study products, the analyzed
population dropped from 3,007,739 down to 172,711 patients.

Beta-blockers among patients with >6 Vioxx and/or Celebrex pickups

COX-2 Concomitancy Analysis Jan 2, 05

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