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.

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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’ claaccumulated 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 8.1Million patients by the number of scripts consumein 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, orBextra script pickupsover 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

Accumulatedscrip 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.

1st Vioxx Script (# of patients)

1st 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 elevatednumbers 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 ana

lysis.

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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