BURLINGTON DRUG CO., INC. v. PFIZER INC.

UNITED STATES DISTRICT COURT

FOR THE DISTRICT OF NEW JERSEY

RE LIPITOR ANTITRUST} MDL No. 2332

LITIGATION

Master Docket No.: 3:12-cv-2389

This Document Relates To: (PGS/IBD)

All End-Payer Class Actions MEMORANDUM

This case is before the Court on End-Payor Plaintiffs’ Motion for Class

Certification. (ECF No. 1251). In this motion, End-Payor Plaintiffs (hereinafter,

“EPPs”) seek class certification on the basis that Defendant Ranbaxy Inc., Ranbaxy

Laboratories Limited, and Ranbaxy Pharmaceuticals, Inc. (hereinafter, “Ranbaxy”

or “Defendant”) engaged in an alleged “reverse payment settlement” with Pfizer

Inc., Pfizer Ireland Pharmaceuticals, Warner-Lambert Company, and Warner-

Lambert Company LLC (hereinafter, “Pfizer”)! which led to the delayed entry of

generic Lipitor. EPPs claim that the effect of Ranbaxy’s agreement is that it blocked

generic drug manufacturers from entering the market earlier, causing EPPs to pay

for Lipitor at an inflated cost for a period of time. EPPs seek monetary damages.

! This Motion was originally filed by both Pfizer and Ranbaxy. In August 2023,

when oral argument was tentatively scheduled, DPPs and EPPs announced their

tentative settlement with Pfizer. As such, Pfizer no longer participated in the motion

practice surrounding this motion. Herein, the Court refers only to the remaining

Defendant Ranbaxy although initial briefing was filed by both Pfizer and Ranbaxy.

On June 20, 2023, EPPs filed a motion to certify two classes of end payors: a

Third-Party Payor (hereinafter, “TPP”) Ciass and a Consumer Class which has two

class periods: the total generic exclusion period (June 28, 2011 to November 29,

2011) and the Generic Overcharge Period (November 30, 2010 to December 31,

2012). Accompanying this motion, EPPs present the reports of two experts: their

ascertainability expert, Ms. Laura Craft (ECF No. 1252-2 (hereinafter, “Craft Rep.”)

and ECF No. 1252-13 (hereinafter, “Craft Reply Rep.”)) and their damages and

antitrust injury expert, Dr. Hal Singer (ECF No. 1252-1 (hereinafter, “Singer Rep.”).

Ranbaxy opposes class certification on several grounds, focusing primarily on

ascertainability and predominance. In support of their opposition, Ranbaxy presents

the report of their expert, Dr. James Hughes (ECF No. 1252-12 (hereinafter,

“Hughes Rep.”)). In addition to these motions, EPPs filed a Motion to Strike the

proposed findings of fact and conclusions of law by Ranbaxy, or in the alternative,

to allow EPPs to file supplemental responses. (ECF No. 1348).2 While Ranbaxy

opposed class certification on multiple grounds, because the Court finds that the

2 EPPs moved to strike a multitude of Ranbaxy’s proposed findings of fact or

conclusions of law or alternatively to allow EPPs to supplement their own findings

of fact and conclusions of law. At oral argument, I denied the motion to strike, but

granted the motion to supplement out of an abundance of caution in undertaking the

rigorous analysis required to determine this motion. (See ECF Nos. 1382, 1387,

1388).

ascertainability requirement of 23(b)(3) is not met, the Court limits its analysis to

the ascertainability prong of Rule 232

EPPs’ proposed classes are vast, encompassing “hundreds of thousands, if not

millions, of consumers, and thousands of third-party payors” across over ten million

brand and generic Lipitor prescriptions written during the class period. (See ECF

No, 815 at 4 488; Singer Rep. at Table 3), EPPs define their two classes as follows:

The Third-Party Payor (“TPP”) Class:

All entities that, for consumption by their members,

employees, insureds, participants or beneficiaries,

purchased, paid and/or provided reimbursement for some

or all of the purchase price of branded Lipitor or generic

atorvastatin calcium, in the Class States, other than for

resale, at any time during the period from June 28, 2011

through and until December 31, 2012.

The TPP Class excludes:

a. Defendants and their subsidiaries and affiliates;

b. Federal and state governmental entities;

c. Medicare Part D Plans; and

d. Medicaid Plans.

The Consumer Class:

Total Generic Exclusion Period (June 28, 2011 through

November 29, 2011). All individuals who purchased, paid

and/or provided reimbursement for some or all of the

3 Nothing herein means that the other class certification requirements have been

satisfied.

purchase price of branded Lipitor, in the Class States,

without the use of a Pfizer co-pay card.

Generic Overcharge Period (November 30, 2011 through

December 31, 2012). All individuals who purchased, paid

and/or provided reimbursement for some or all of the

purchase price of generic atorvastatin calcium, in the Class

States.

The Consumer Class excludes:

a. Judges assigned to this case and their chambers’

staff and any members of the judges’ or chambers

staff's immediate family;

b. Defendants’ officers, directors and employees;

c, Individuals who only purchased through a Medicare

Part D or Medicaid Plan;

d. Individuals who only purchased branded Lipitor

after November 30, 2022, and did not purchase

generic atorvastatin calcium; and

e, Any “flat copay” consumers who purchased Lipitor

only via a fixed dollar copayment that does not vary

on the basis of the drug’s status as brand or generic.

There are two principal reasons why this motion for class certification must

be denied. First, the Court granted summary judgment, finding that there was no

genuine issue of material fact as to an essential element of the cause of action:

causation. (ECF No. 1415). Causation—which is inextricably linked with antitrust

injury——prevents the Court from certifying a class where causation cannot be shown.

There is no cause of action, and accordingly, there is no class. Second, even if

summary judgment had been denied and the Court had found that there was a

genuine issue of material fact, class certification would still be inappropriate because

EPPs have failed to show the ascertainability of their proposed class under Federal

Rule of Civil Procedure 23(b)(3).

Ascertainability is discussed below.

1.

“The class action is ‘an exception to the usual rule that litigation is conducted

by and on behalf of the individual named parties only.’” Wal-Mart Stores v. Dukes,

564 U.S. 338, 131 (2011) (quoting Califano v. Yamasaki, 442 U.S. 682, 700-01

(1979)). The party seeking certification must establish each element of Rule 23 by

a preponderance of the evidence. In re Hydrogen Peroxide, 552 F.3d 305, 307 (3d

Cir. 2009), “‘[A]ctual, not presumed, conformance’ with Rule 23 is essential.” Jd.

at 326 (internal citations omitted). To determine whether actual conformance with

Rule 23 has been met, the Court is obligated to conduct a “‘rigorous analysis’” of

the evidence and arguments presented. Jd. 316. In performing this analysis, courts

must resolve all factual or legal disputes relevant to class certification even if they

overlap with merits issues. /d. at 307.

The Third Circuit has “repeatedly ‘emphasize[d] that [a]ctual, not presumed

conformance’ with Rule 23 requirements is essential.” Gonzalez vy. Corning, 885

F.3d 186, 192 (3d Cir. 2018) (internal citations omitted). “When courts harbor doubt

as to whether a plaintiff has carried her burden under Rule 23, the class should not

be certified.” Mielo v. Steak ‘a Shake Operations, Inc., 897 F.3d 467, 483 (3d Cir.

2018) (citing In re Hydrogen Peroxide, 552 F.3d at 321)).

In our Circuit, where the parties seek certification on the basis of Rule

23(b)(3), EPPs must satisfy the requirements of ascertainability, predominance, and

superiority. See In re Niaspan Antitrust Litig., 67 F.4th 118, 133 (3d Cir. 2023).

With respect to ascertainability, the class must be “‘currently and readily

ascertainable based on objective criteria.” Hargrove v. Sleepy’s LLC, 974 F.3d 467,

477 (3d Cir, 2020).

This ascertainability requirement is two-fold: “‘(1) the class is defined with

reference to objective criteria; and (2) there is a reliable and administratively feasible

mechanism for determining whether putative class members fall within the class

definition.’” Jd. at 469-70 (internal citation omitted), As the Third Circuit has

explained, the ascertainability requirement is:

grounded in the nature of the class-action device itself. In endeavoring

to further explain this concept, we adhere to the precise boundaries of

ascertainability previously iterated in the quartet of cases we discuss

below. The ascertainability requirement as to a Rule 23(b)(3) class is

consistent with the general understanding that the class-action device

deviates from the normal course of litigation in large part to achieve

judicial economy.

Byrd v. Aaron’s Inc., 784 F.3d 154, 162 (3d Cir. 2015) (internal citations omitted).

Indeed, the ascertainability requirement provides due process “by requiring a

defendant to test the reliability of the evidence submitted to prove class

membership.” Carrera v. Bayer Corp., 727 F.3d 300, 307 (3d Cir. 2013). As such,

a defendant has a due process right to raise challenges to the proof proffered to

demonstrate class membership. See id. (citing Marcus, 687 F.3d at 594).

To satisfy the ascertainability requirement, a plaintiff must present a

methodology to identify class members and prove by a preponderance of the

evidence that such methodology will not require extensive and individualized

inquiry or mini-trials. Marcus, 687 F.3d at 593; see also Byrd vy. Aaron’s Inc,, 784

F.3d 154, 163 (d. Cir. 2015) (“We explained, ‘If class members are impossible to

identify without extensive and individualized fact-finding or ‘mini-trials,’ then a

class action is inappropriate.’”). Importantly, identification in this context does not

mean that “a plaintiff must be able to identify all class members at class

certification—instead, a plaintiff need only show that ‘class members can be

identified.’” Byrd, 784 F.3d at 163 (internal citations omitted) (emphasis in the

original).

“Administrative feasibility means that identifying class members is a

manageable process that does not require much, if any, individual factual inquiry.’”

Carrera, 727 F.3d at 306 (internal citations omitted). A “plaintiff must propose a

classification method with evidentiary support... Jn re Niaspan Antitrust Litig.,

67 F.4th 118, 130 (3d Cir. 2023) (hereinafter, “Niaspan IH”). The Third Circuit has

made clear that “[a]ffidavits, in combination with records or other reliable and

administratively feasible means, can meet the ascertainability standard.” City Select

Auto Sales Inc. v. BMW of N. Am. Inc,, 867 F.3d 434, 441 Gd Cir. 2017) (internal

citations omitted); see also Byrd, 784 F.3d at 171.

Il.

A fulsome discussion of the facts underlying this litigation is recited in the

Court’s Memorandum on Summary Judgment. (ECF No. 1415). For the purposes

of this motion, the Court discusses the facts as related to EPPs’ proposed

methodology within its analysis of the ascertainability prongs below.

i. EPPs’ Proposed Methodology

To determine whether ascertainability under 23(b)(3) is satisfied, the Court

must analyze the EPPs’ methodology to ascertain the class, eliminate exclusions,

and see if the methodology is administratively feasible—i.e., if it is “manageable

process that does not require much, if any, individual factual inquiry.” Carrera, 727

F.3d at 306 (internal citations omitted). To answer this question, one must

understand the prescription pharmaceutical payment flow.

This flow is complex. TPPs are entities that may pay for part or all of the cost

of members’ prescription drug. Insurers, self-funded plans, fully-insured plans, and

union health and welfare funds are most often considered TPPs.

The flow of payments for prescription pharmaceuticals often involves

multiple parties, including manufacturers, wholesalers, retail and mail-order

pharmacies, consumers, federal and state payors, aid organizations, insurers, health

plans, Pharmacy Benefit Managers (hereinafter, “PBMs”), Third-Party

Administrators (hereinafter, “TPAs”), and Administrative Services Only providers

(hereinafter, “ASOs”). (Hughes Rep. at 933). The final purchase in this distribution

chain is called the “end purchase;” this is the final sale of a drug to the ultimate

consumer who will use the prescribed drug. (See Nov. 28, 2023 Tr. at 27:9-16).

Before a pharmacy dispenses a prescription drug to a consumer, the pharmacy

determines, among, other things, who the end-payor is, whether the drug is covered

by the consumer’s prescription drug benefits, and how much will be paid by the

consumer and the TPP. (See Craft Rep. at 4 28; see also Nov, 28, 2023 Tr, atT27:9— □

T28:10), The cost of a prescription drug depends on whether the consumer is a cash

payor or whether the consumer has prescription drug benefits that pay for a portion

of the drug’s cost. (Craft Rep. at J§ 13, 28-29; Nov. 28, 2023 Tr. at T27:18-21;

T31:23~—T32:10).

The consumer and the pharmacy need to know on a real-time basis who is

paying for a drug and, if applicable, the amount a consumer will co-pay. As such,

pharmacies communicate with PBMs. PBMs play a central role in the

pharmaceutical distribution chain by acting as intermediaries between drug

manufacturers, pharmacies, health plans and commercial insurers. (Hughes Rep. at

44). PBMs do not pay for the purchase of a prescription drug. (See Nov. 28, 2023

Tr. at T31:23—-T32:10). Rather, “PBMs are intermediaries who operate on behalf of

their clients in transferring funds. They are not payors.” (See ECF No, 1259-2

(hereinafter, “Craft Dep.”) at T35:22-T136:18). Overall, PBMs manage the large

volume of pharmacy claims by processing and adjudicating claims on behalf of

health plans and payors in a process known as “claims adjudication.” (Nov. 28, 2023

Tr. at T28:15~—20; Hughes Rep. at 44).

ASOs and TPAs are intermediaries that facilitate the claims adjudication

process for TPPs. ASOs and TPAs provide no payment towards pharmaceutical

purchases, (Craft Rep. at {f 36-39; see also Nov. 28, 2023 Tr. at T33:3-14; T38:19-

22). ASOs are also “insurance companies that provide administrative services for

clients that prefer to self-fund their benefits,’ and TPAs are “non-insurers that

provide similar services for self-funded clients.” (Craft Rep. at 36; see also ECF

No. 1252-5 (hereinafter, “Fridberg Declaration”) at 15). By their nature, ASOs

and TPAs are not end-payors because they do not contribute any amount to the

ultimate purchase of the prescription drug. (Nov. 28, 2023 Tr. at T38:19-22).

Working against this backdrop and to account for the prescriptions issued,

there are federal laws and regulations, including the Health Insurance Portability and

Accountability Act (hereinafter, “HIPAA”), that require record keeping. These laws

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and regulations mandate that prescription data be transmitted and retained in a

standardized format pursuant to National Council for Prescription Drug Programs

(hereinafter, “NCPDP”) standards. (See Nov. 28, 2023 Tr. at T29:7-T30:8, 35:8—

12; Craft Rep. at 9] 20-24, 28-29). It is the data protected by HIPAA and these

regulations that is the basis for the methodology in this case.

This methodology was elaborated by EPPs’ ascertainability expert, Ms. Laura

Craft. She opined that the members of the class could be identified and that

individuals could be excluded from the class through the data produced from the

above prescription process. Overall, she reached the following conclusions. First,

industry practices and legal mandates result in the creation, standardization, and

retention of detailed data for prescription drug dispensing. (Craft Rep. at 4 16).

Second, this data is centralized by PBMs. (Craft Rep. at 17). Third, TPP and

consumer data can also confirm class membership. (Craft Rep. at 918). Fourth,

class exclusions can be applied using multiple information sources. (Craft Rep. at J

19). To support these conclusions, Ms. Craft analyzed a quantity of information.

Ms. Craft analyzed data and documents from PBMs, TPPs, and consumers specific

to purchase of Lipitor and generic Lipitor. PBM data was sourced from three

PBMs—Prime Therapeutics, LLC, Humana Pharmacy Solutions, and Express

Scripts, Inc. (Craft Rep. at ¢ 11). TPP data was also sourced from one insurer, Blue

Cross Blue Shield of Louisiana, and seven self-funded plan sponsors (A.F. of L. —

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A.G.C. Building Trades Welfare & Pension Plans; Bakers Local 433 Health Fund;

the Mayor and City of Baltimore, Maryland; the Fraternal Order of Police, Fort

Lauderdale Lodge 31; the United Food and Commercial Workers & Employers

Arizona Health & Welfare Trust; the Twin Cities Bakery Workers Health and

Welfare Fund, and the City of Providence). For the Consumer Class, Ms. Craft

analyzed data supplied by five consumers (Edward Czarnecki, Emilie Heinle,

Andrew Livezey, Jean Dougan, and Nancy Billington), (/d.). With respect to the

Pfizer co-pay program, Ms. Craft “was given access to and reviewed documents

pertaining to the copay card program for branded Lipitor offered by Defendant.”

(Id.). She also reviewed depositions of some of these plaintiffs and Declarations of

executives from PBMs as well as a claims administrator. (See id. at Ex. 3). Before

reviewing Ms. Craft’s findings, the data provided by these entities and individuals

is examined.

As previously stated, the claims adjudication process was detailed by

Declarations provided by several PBM executives. For example, the PBMs

described the scope of their coverage. Deb Fridberg, AVP of PBM Operations for

Prime Therapeutics (hereinafter, “Prime”), explained that Prime provides PBM to

33 million people including Blue Cross and Blue Shield. (See ECF No. 1252-5,

(hereinafter, “Fridberg Declaration”) at ¥ 4). Similarly, Edward Devaney, Senior

Vice-President of Caremark, L.L.C. (hereinafter, “Caremark”), another PBM, noted

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that Caremark managed approximately 2.2 billion prescriptions in 2021. (See ECF

No. 1252-6 (hereinafter, “Devaney Declaration”) at 3).

Also detailed in Ms. Fridberg’s and Mr. Devaney’s Declarations is the actual

flow of the claims adjudication process—specifically, that the claims adjudication

process involves detailed information that is electronically exchanged between a

pharmacy and PBM. This information includes who made the purchase, what

product was purchased, when the purchase occurred, where the drug was purchased

location of the dispensing pharmacy, location of recipient consumer), and how

much each EPP paid for the prescription drug (7.e., the respective proportion of the

total purchase price the consumer and TPP paid). (See Nov, 28, 2023 Tr. at T28:21—

T32:10; Craft Rep. at Ff 21, 28-30).

Where PBMs handle data that surrounds claims involving ASOs and 'TPAs,

Prime and Caremark stated the following. Ms. Fridberg certified that “for claims

involving a TPA or ASO, Prime maintains data indicating the name of the TPA or

ASO and the self-funded payor client of the ASO or TPA. Prime can associate these

Carrier and Account IDs with Carrier and Account Names.” (Fridberg Declaration

at J 16). Similarly, Mr. Devaney stated in his Declaration that “[t]he claims-related

data can also be sorted with respect to transactions . . . involving Clients acting in an

ASO or TPA capacity.” (Devaney Declaration at § 10). Mr. Devaney asserted that

13

“senerally” the claims can be routed to exclude single-tier flat co-pay structure, state

or federal agency, and ASO/TPA capacity to identify the ultimate end-payor.

(Devaney Declaration at 9). Notably, both Prime and Caremark did not produce

any data in this case identifying the ASO/TPA, or ultimate end-payor transactions.

(Nov. 28, 2023 Tr. at T70:5—16; see also Craft Rep. at [37). There is no indication

that the PBMs would be able to produce such data, and EPPs have not been able to

corroborate the PBM executives’ statements. As follows, Defendant has not been

able to substantively respond to these statements.

In addition to PBM data, Ms. Craft points to other data sources that are useful

in identifying inclusions in the class. For example, she points to documents

produced by named TPP class plaintiff Bakers Local 433 Health and Welfare Trust

(hereinafter, “Bakers Local 433”). The Bakers Local 433 data identifies the

purchaser, the drug purchased, when it was purchased, where it was purchased, and

for how much it was purchased. (See Nov. 28, 2023 Tr. at T36:22-T37:21; Craft

Rep. at {| 65-67). Thus, in this instance, she relied on TPP data as opposed to PBM

data.

In an example of another TPP data source, Ms. Craft refers to data from New

Mexico United Food and Commercial Workers (hereinafter, “NMUFCW”), a named

TPP Class plaintiff which employs a TPA to administer its prescription plan benefits.

14

The data from NMUFCW identifies the payor, the date of the transaction, the product

purchased, and the location of the of the transaction. (Nov. 28, 2023 Tr. at T39:18—

T40:25),

Ms. Craft bolsters her argument about the identification of class members

through the use of the claims administration process. Specifically, Ms. Craft opines

that class membership can be determined in an administratively feasible manner by

using the routine claims administration process that includes claim forms containing

affidavits. For this, Ms. Craft relies upon the Declaration of Mr. Eric Miller, Senior

Vice President of Case Management with A.B. Data, Ltd.’s Class Action

Administration Company to support the proposition that claims forms can verify

information contained within the data provided to Plaintiffs. Mr, Miller stated:

As part of the claims process, A.B. Data regularly obtains data from

one or more of the multiple highly standardized and electronically

stored sources of prescription transaction data including TPP records,

pharmacy records, and records maintained by other third parties such

as ASOs, TPAs and PBMs to identify class members and verify class

membership, including that claimants purchased the drug(s) in question

during the relevant time period, within class states (if necessary), and

that they are not otherwise excluded from the class.

(ECF No. 1252-3 (hereinafter, “Miller Declaration”) at 712). Notably, Mr. Miller

did not explain what the highly standardized and electronically stored sources are.

In his Declaration, Mr. Miller stated that claims forms can verify information

contained in the data and determine class membership. (Craft Rep. at □ 15; Miller

Declaration at J] 2-3; 10-22). The process is described by Mr. Miller as follows.

15

A.B. Data has provided “notice to consumer{s] in cases involving pharmaceutical

products” by various methods, including “direct mail notice by First Class U.S. mail

to class members identified in PBM data and the data of other subpoenaed entities,

such as TPPs, retail pharmacies and mail order pharmacies” and “[p]ublishing notice

in targeted publications comprised of digital media and earned media. This includes

placing digital banners, text and/or newsfeed ads on sites such as Google, Facebook

and/or Instagram and targeting medical networks, including websites such as

WebMD.com.” (See Miller Declaration at {J 8a—8b). According to Mr. Miller, A.B.

data creates a case-specific website for claimants to fill-out. (Miller Declaration at

(11).

Claims information is obtained from multiple “highly standardized and

electrically stored sources of prescription transaction data including TPP records,

pharmacy records, and records maintained other third parties such as ASOs, TPAs

and PBMs....” (Miller Declaration at | 12). In addition, the claims form has a

separate “authorized agent section” for TPAs and ASOs to fill out and to set forth

the class member section it represents. (Miller Declaration at { 14). There is also a

class member section “to capture information from the class members that will assist

in determining eligibility” based upon the information provided in this form. (Miller

Declaration at J 15).

16

Examples of these claims forms were provided to the Court. (See ECF No.

1325-2). For example, EPPs submitted a model TPP claim form. On the top of the

form, it reads: “Instructions for submitting your Third-Party Payor Claim Form,”

explaining that a TPP class member or an authorized agent can complete the form.

(id. at 78). The form notes relevant exclusions, stating that a TPP class member

does not include: Defendants and their subsidiaries and affiliates; federal and state

governmental entities; Medicare part d plans; and Medicaid plans. Ud. at 79). The

form also outlines what information should be provided. EPPs had a prospective

TPP class member itself—Bakers Local 433—-complete this form. Bakers Local

433 indicated that it was a “Self-Insured Health & Welfare Fund” and indicated that

“the total amount paid or reimbursed for prescriptions of branded Lipitor and/or AB-

rated generic Lipitor, net of co-pays, deductibles and co-insurance between June 28,

2011 and December 31, 2012” was $43,251.87. (Ud. at 82). Finally, in Section D,

the form requires that claim documentation be attached for claims of $300,000 or

more, also noting that data might also be required. (Ud). In Section E, there is a

certification of truthfulness to be executed. EPPs also had an identical form

completed by a TPP’s registered agent. (/d. at 86). This document was completed

by registered TPP agent Southwest Service Administrators, Inc. (hereinafter,

“SSA”) on behalf of United Food and Commercial Workers. The completed form

indicated that SSA certified that “the total amount paid or reimbursed for

17

prescriptions of branded Lipitor and/or AB-rated generic Lipitor, net of co-pays,

deductibles and co-insurance between June 28, 2011 and December 31, 2012” was

$32,759.18. Ud. at 90).

Finally, there was a consumer claim form completed by named plaintiff,

Nancy Billington. (/d. at 102), The document contains instructions for submitting

the consumer claim form, breaking down the process step-by-step. There is an

explanation of the class on page two of the form, stating that “you are a member of

the Consumer Class if you purchased, paid, and/or provided reimbursement for some

or all of the purchase price of branded Lipitor, without the use of a Pfizer co-pay

card” in the Class states during the June 28, 2011 through November 29, 2011

timeframe. (7d. at 103 (emphasis in the original)). The form also clarifies that you

ate a member of the Consumer Class if “you purchased, paid, and/or provided

reimbursement for some or all of the purchase price of generic Lipitor” in the Class

states during the November 30, 2011 through December 31, 2012 timeframe. Ud. at

103 (emphasis in the original)). The certification lists all the exclusions, and Ms.

Billington certified that she was not within any of the exclusions. One issue with

this claims form is that it assumes that the consumer will understand the distinction

between “branded” and “generic” Lipitor without defining these terms.

Overall, both the TPP and Consumer Class claims forms acknowledge that

the claims administrator will review these submissions and will follow-up with the

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claimant to either obtain necessary information to process the claim or to terminate

the claim. This is an area where follow-up is necessary to determine the inclusions

or exclusions within the class. The claims administrator implied the same within his

Declaration—-stating that there would be times in the claims process where

individual calls to claimants would be needed “should additional information or

documentation be required.” (Miller Declaration at § 20). Mr. Miller also stated

that A.B. Data “is capable of performing additional checks.” (Cd. at 22). What

these additional checks are, however, is not explained. After claims administrators

compile the claims information submitted in the claims forms, the claims

administrator transmits the data with appropriate codes to Ms. Craft’s firm where it

is “submitted ... in a templatized form provided by the claims administrator that is

read by a machine.” (Nov. 28, 2023 Tr. at T70:17—25).

Along with the identification of class members comes its natural counterpart:

class exclusions. This is one of the most disputed areas of Ms. Craft’s report.

I. TPP Exclusions

In terms of the TPP exclusions, Ms. Craft provides the following ways to

identify exclusions:

a. Defendants and their Subsidiaries or Affiliates

Ms. Craft explained that this proposed exclusion is “a common exclusion that

should be easily applied.” (Craft Rep. at { 83). She states further that “Defendants

19

are well aware of each entity encompassed by this exclusion and can provide a list

of entities to be excluded from the class. Once a list is generated, the claims

administrator can easily exclude such entities as has been done in other class

actions.” (d.).

b. Governmental Entities

Ms. Craft provides three ways to identify the governmental exclusions: (i) by

having PBMs identify, flag or exclude transactions in which a federal or state

government entity is the payor; (ii) by cross-checking a list of federal or state

government payors against the names of the data and in the claim forms that are

filed; and (iii) by requiring claimants to verify via an affidavit on claim form, under

penalty of perjury, that they are not a federal or state government payor. (Craft Rep.

at Ff 84-90; Nov. 28, 2023 Tr. at T42:18-T47:8).

Ms, Craft goes into detail regarding the cross-checking of the sources, stating

that in addition to the certification on the claims forms, data can be verified through

other data sources. For example, Ms. Craft states that, aside from Medicare and

Medicaid, there are five other major federal government programs: (1) Department

of Defense/Tricare, (ii) the Department of Veterans Affairs, (iii) the Indian Health

Service, (iv) the Children’s Heaith Insurance Program, and (v) Ryan White ADAP.

She states that “almost all of the federal government’s prescription drug spending is

accounted for by these major programs.” (/d. at □ 85). Ms. Craft opines that the

20

major federal programs—with the exception of the Veterans Administration—use

PBMs, and data from PBMs can identify the programs with field identifiers in the

data such as HIS, CHIP, TRICARE, DoD, and ADAP. (7d).

Ms. Craft also states that third party sources such as Milliman, Inc., a benefits

consultant and actuary; the National Conference of State Legislatures (hereinafter,

“NCSL”); or State Offices of Personnel; can assist in identifying state government

entities. (Craft Rep. ff 89-90; Nov. 28, 2023 Tr. at T42:18-T47:8). Specifically,

she stated that NCSL, using information from Milliman, has identified states “where

the government entities themselves provide the health benefit plan as distinguished

from those where the state just pays premium to a commercial insurer to cover its

employees[,|” consequently helping to cull the information that the claims

administrator needs to review. (Craft Rep. at J 89). Thus, where follow-up here is

necessary, Ms. Craft states PBMs have an internal process for tracking and filtering

out government payors, which can be used to cross-check the data and claim forms

submitted by claimants. (See Craft Rep. □□ 89-90; see also Fridberg Declaration at

q 14 (“Prime also maintains information that would allow it to identify, flag, or

exclude transactions for which a state or federal governmental entity is the payor.”)).

c. Medicare Part D and Medicaid Plans

In applying exclusions to the Medicare and Medicaid Plans, Ms. Craft states

that PBM data can help identify these exclusions. Specifically, she states:

21

PBM data consistently and explicitly identifies Medicare and Medicaid

plans, sometimes in a “line of business” field and sometimes in plan

description fields. There is no ambiguity in selecting and excluding

claims associated with these plans. Medicare Claims are typically

described as MEDD (Medicare Part D), PDP (freestanding Medicare

Prescription Drug Plans that do not include health benefits), MAPD

(Medicare Advantage Part D which combines health and prescription

drug benefits), EGWP (Employer Group Waiver Plans, a telm

reselved for a customized folm of Medicare prescription dmg plans),

or RDS (Retiree Dmg Subsidy, another variant on Medicare Part D

plans). Medicare plan sponsors must enter into contracts with CMS

which are listed on a CMS website. Similarly, Medicaid plans operated

by the states are consistently identified by that telm or a recognizable

acronym (eg., MCAID) in PBM data, typically followed by the

abbreviation of the state which the plan serves.

(id. at 91). Ms. Craft states that using Prime data is particularly helpful in this

pursuit since the “Client Type” field identifies the line of business associated with

each claim. (/d. at 992). Accordingly, by filtering by the “Client Type” field, one

can determine the number of claims that are for Medicaid (“MCAID” field) or

Medicare (“MCARE” field) plans and would be excluded from both the TPP and the

Consumer Classes. (/d.).

d. ASOs, TPAs, and fully insured health plans

In terms of identifying whether an ASO, TPA, or a fully-insured health plan

was involved in the transaction (one of the implicit exclusions to the EPP class), the

claims administrator must evaluate whether an ASO or TPA was involved in the

transaction to determine an exclusion. The process of accomplishing this is

explained through Declarations from employees who work for ASOs, TPAs, and

22

PBMs who confirm that their data can be adjusted to show whether an ASO or a

TPA is involved in a transaction. Mr, David W. Perret, IIL, the Director of Pharmacy

Operations at Blue Cross Blue Shield of Louisiana (hereinafter, “BCBSLA”)—an

insurer and ASO—-stated that BCBSLA “can identify and produce data regarding

prescription drug transactions by Line of Business. The data includes the name and

other identifying information for all plans, including the plans for which BCBSLA

is acting in an ASO capacity.” (ECF No. 1252-14 (hereinafter, “Perret Declaration”)

at 11).

Ms. Shawn Lovering, an account manager at Southwest Service

Administrators Inc, (hereinafter, “SSA”), a TPA for self-funded clients—including

one named plaintiff—stated: “In my experience working with ten different PBMs,

the TPA will provide information—including name and contact information—to the

PBM regarding the underlying client on whose behalf it is providing TPA services,

and the funding status of that client.” (ECF No. 1252-15 (hereinafter, “Lovering

Declaration”) at ] 9). Ms. Lovering also identifies:

In the claims data files provided by the PBM, the name of the

underlying self-funded plan sponsor is typically reflected in the data

field titled “CARRIER NAME?” if the client has directly contracted

with PBM, or “ACCOUNT NAME” OR

“RMPLOYER. GROUP NAME” if the TPA contract the PBM on

behalf of its client.

(Lovering Declaration at] 10). Other PBMs state the same. Ms. Tamara Cowley,

the Associate Director of Business Intelligence at Humana, a PBM, states:

23

Humana also maintains information that HPS has access to that would

allow it to identify transactions where Humana, or the client it is

contracting with, is acting in an ASO or Third Party Administrator

(“TPA”) capacity. Where the client is an ASO or TPA, Humana

maintains data that would allow for the identification of the underlying

self-insured payor.

(ECF No. 1252-4 (hereinafter, “Cowley Declaration”) at 6). Ms. Craft states that

the HPS data contains fields for each transaction needed to determine class

membership, including exact product dispensed (“NDC” field), the date of the

transaction (“SERVICE DATE” field), pharmacy type

(“RX NTWK_TYPE DESC” field), the pharmacy location (““PHAR_STATE”

field), and the member’s residence state (“MBR_STATE” field)—applicable for

those claimants who filled prescriptions by mail order. (Craft Rep. at | 45). The

total payments to the pharmacy are broken out into the product cost

(“PHAR_INGR_COST_AMT” field). (d.). The dispensing fee paid to the

pharmacy by the TPP is also represented (“PHAR DISP_FEE AMT” field) as well

as any sales tax charged (“PHAR SALES TAX AMT” field). (Ud.). According to

Ms. Craft, this “is a standard structure in the industry although the field names may

vary slightly.” (d.).

2, Consumer Class Exclusions

In terms of the Consumer Class exclusions, Ms. Craft provides the following

ways to identify exclusions:

a. Pfizer Co-Pay Card

24

To qualify as a member of the Consumer Class, a consumer must have made

at least one of their Lipitor purchases during that time period without the use of a

Pfizer Co-Pay card. In terms of identifying individuals who made purchases with a

Pfizer Co-Pay Card, Ms. Craft declares that Pfizer created and maintained data

related to the Pfizer Co-Pay Cards. Specifically, she states the following:

When the Co-Pay Card was used, Pfizer would directly reimburse the

pharmacy for a portion of the consumer’s co-pay and the pharmacy

would collect less from the consumer at point of sale, whether mail

order or retail. This payment by Pfizer was processed as an “off-line”

transaction between the pharmacy and Pfizer’s program administrator

and does not appear in the PBM or TPP data. However, the very nature

of the program necessitated the collection and maintenance of

electronic data linked to individual consumers each time it was used.

The Co-Pay Cards were only accepted at participating pharmacies;

where members sought reimbursement through the program at non-

participating pharmacies, they were required to send their name,

prescription, mailing address, as well as a copy of their Co-Pay Program

Card to Pfizer to qualify for reimbursement. Activating a Co-Pay Card

at a participating pharmacy required identification of the consumer and

specifically linked their Lipitor purchases to them.

(Craft Rep. at JJ 40-41).

Although EPPs are not in possession of discovery concerning Pfizer’s Co-Pay

Card program,’ Ms. Craft relies upon a Pfizer presentation wherein the Lipitor $4

Co-Pay Card was identified together with data including the “Redemptions by

Patient Out of Pocket Cost,” showing the percentage of redemptions among “Tier |

4 At some point in time, Pfizer transferred its coupon program to Viatris, Inc. Viatris,

Inc. may possess the records regarding the coupon program. However, EPPs have

failed to subpoena them. (ECF No. 1259 at 32 n.12).

25

$0-$15,” “Tier 2 >$15-$30,” “Tier 3 >$30-$90,” and “Cash>$90.” (Craft Rep at J

43). Ms. Craft concludes—without having reviewed the Pfizer Co-Pay Card

program data—that this presentation demonstrates that Pfizer kept close track of the

number of redemptions, including how much was spent, how often the redemptions

occurred, and the specifics of the patients who made redemptions. (/d.)

b. Judges, Chambers’ staff, Defendants’ officers, directors, employees and

their immediate families

Like the first TPP exclusion,’ Ms. Craft explained that this proposed exclusion

is simple to apply and that a list of individuals encompassing these groups can be

provided and supplied to the claims administrator to cross-check the names in the

lists and ensure that the consumers do not fall into one of these categories. (/d. at |

95).

c. Individuals who purchased through a Medicare Part D or Medicaid

Plan

Like the Medicare and Medicaid exclusion in the TPP class, Ms. Craft

explained that the same process can be used to identify to filter through the consumer

class exclusions within this category. (Ud. at ¥ 96).

d. Individuals who only purchased branded Lipitor after November 30,

2011 and did not purchase generic atorvastatin calcium

Ms. Craft explains that this exclusion can be determined as follows:

> See supra at 19-20.

26

a consumer did not purchase branded Lipitor during the Total Generic

Exclusion period without the use of a co-pay card, it must have bought

generic atorvastatin during the Generic Overcharge period to be a

member of the Proposed Consumer Class. Either the data provided by

the PBM, the TPP, or the consumer itself can be used to confirm that

the consumer made at least one qualifying purchase of generic Lipitor

after November 30, 2011. Purchases by a single enrollee are linked by

their Member ID, thus allowing for the tracking of purchases across

time.

(id. at 4 97). Ms. Craft states that this is a “straightforward analysis” which is

demonstrated by the data and documents produced by individual named consumers

in the present litigation.

e. “Flat Co-Pay” consumers

With this exclusion, Ms. Craft states that it applies to “at most a very small

set of consumers whose benefit plan requires a co-payment that does not vary based

on the drug’s status as a brand or generic.” (/d. at { 98). To support her assertion

that the number of consumers falling into this category is small, Ms. Craft cites a

2011 Employer Health Benefits Annual Survey by the Kaiser Family Foundation

and Health Research & Educational Trust, stating that “only 7% of covered workers

in the U.S. had single-tier drug plans (excluding specialty tiers) that treat all drugs

equivalently.” (/d. at { 99). Further, she stated that of those workers, “only 24%

[were] subject to a copayment structure rather than a percentage co-insurance

structure.” (d.). Ms. Craft further explained that:

even for non-specialty drugs, at most 1.68% of covered workers could

theoretically have the same flat copay for both brand and generic drugs

27

(7% x 24%) = 1.68%), assuming that both were covered. However,

single tier plans typically employ closed formularies that remove the

brand entirely once a generic becomes available.

(id.). From this, she concludes that “there are no, or almost no consumers whose

claims would be subject to the same flat co-pay for the brand and generic.” (/d.).

In determining how the flat co-pay exclusion can be confirmed, Ms. Craft

states that the claims administrator can use “data and certifications to compare brand

and generic copay amounts.” (Craft Rep. at | 98; Nov. 28, 2023 Tr. at T52:21—

T53:17; see also Devaney Declaration at { 9 (“The claims data can generally be

sorted to exclude, for example, members of plans having single-tier flat co-pay

structure (i.e, where member pays same fixed co-pay amount... for any

prescription received ....”)).

3. Claims Administrator

Overall, the application of these exclusions across both classes is aided by

input from claims administrator, A.B. Data. The claims administrator is tasked with

culling the data and cutting the groups who are excluded from the proposed claims.

According to Mr. Miller, A.B. Data provides the following solution to applying

exclusions:

1) Defendants, Judges, and Related Persons and Entities: . .

_. Lf provided with a list of entities that are specifically excluded

from recovering in this matter, A.B. Data can cross-reference any

claims it receives against that list. Claims forms in similar matters

also typically require the claimant to certify that it is not one of the

listed ineligible entities[;]

28

2) Federal and State Government Entities: ... , During the

claim submission process, any entity that submits a claim would be

required to certify that it is not a federal or state entity .... [3]

3) Fully insured Prescription Drug Plan: ....1n other similar

matters, during the claim submission process, entities are required

to certify that their claim was not fully insured through another

entity during the class period....

4) Consumers who never purchased the generic during the

class period: .. . The data can be used to verify that a consumer

claimant purchased the generic during the class period, The claim

form can also be used to obtain additional details and certifications.

For example, the claim form could request that the consumer enter

the total amount they paid for purchases of the brand drug, and

separately, the total amount they paid for purchases of the generic

drug. If the claimant inputs $0 in the generic field or leaves that

field blank, a follow-up letter could be sent to the individual to

confirm that they did not purchase the generic or to request

additional documentation demonstrating that they purchased the

generic product. In addition, the claimant would be required to

certify that they purchased the generic product during the class

period|; |

5) Consumers with a flat co-pay structure: ....'The data and

certifications can also be used to verify that a consumer claimant did

not have a flat co-pay structure by comparing the copayment

amounts for their brand and generic purchases. In addition, the

claimant would be required to certify that they did not purchase

under a plan that had a flat co-payment structure. Ifa PBM, TPA or

ASO supplies the data, they can be asked to specify if any plans had

a flat co-pay structure[;]

6) Medicare Part D and Medicaid Plans: .... {T]he claim

form can include a requirement that the claimant certify that

coverage was not supplied by one of these two plan types.

(Miller Declaration at ff] 24-29).

29

ii. Defendant’s Response

In response, Defendant argues that EPPs have not demonstrated a reliable and

administratively feasible method to identify class members or to apply class

exclusions. Further, Defendant argues that the proposed methodology has not been

shown to be able to be implemented without excessive cost.

With regards to the method for identifying members, Defendant argues that

the ascertainability methodologies rely almost exclusively on unsupported opinions

and consist of the “same kind of conclusory assertions and promises of future

compliance that the Niaspan I and other courts in the [Third Circuit have]

consistently reject[ed]... .” (ECF No. 1259 at 19). Overall, Ranbaxy argues that

these mere assurances that “‘available data’ can be used later, together with

affidavits to confirm class membership is insufficient.” (Ud. at 21). Essentially,

Defendant argues that the class member identification process is speculative as it

“amounts to nothing but bare reliance on claims form certifications . . . in a post-

liability claims administration process[,]” and relies on conclusions from an expert

who did not review actual claims forms in the process. (/d. at 22). Defendant also

takes issue with EPPs’ method of applying exclusions, specifically with respect to

governmental entities and ASOs, TPAs, and fully-insured health plans for TPPs and

Pfizer Co-Pay Card Consumers and flat co-pay card consumers for the Consumer

30

Class. Against this backdrop, Defendant argues that the application of this alleged

methodology is unfeasible given the cost.

Defendant repeatedly points out the fact that Ms. Craft does not appear to have

a methodology. For example, at her March 2023 deposition, when asked about her

methodology, Ms. Craft testified that her expert report “identiflies] the kinds of

information that can be used to address each of the criteria. I don’t know whether

that was described as a methodology in the report.” (Craft Dep. T68:21-T70:20).

She testified that “I think what I have described is a methodology for obtaining the

information that would confirm that each of the criteria for class membership is

met.” Cd, at T71:4—18).

During oral argument on November 28, 2023, Defense Counsel questioned

Ms. Craft regarding whether she had “laid out a step-by-step methodology by which

the available data can be systematically analyzed to identify class members . . .”

(Nov. 28, 2023 Tr. at T57:18-20). Ms. Craft responded that:

the data is to be produced containing all of the essential fields to

determine class membership. That data can then be, literally by a

computer, analyzed to identify any claims that do not comply with the

basic conditions of the class definition. And if there are any that do not

comply, the claims administrator has, in my proposed methodology, the

contact information to follow up with the entity that was submitting it,

to see if further information is necessary or to resolve any issue.

(Ud. at T57:21—T58:5).

31

Defendant’s expert, Dr. Hughes, similarly hones in on this issue, criticizing

EPPs’ lack of methodology. At oral argument, Dr. Hughes opined:

Ms. Craft says the data are available from a number of sources to verify

class membership. This is not methodology, it’s not a system, not a set

of steps that one would go through to identify the class members. It’s

just simply saying, well, the data are all there that must be in there, and

I guess now—or she’s not going to go find it, but the potential class

members themselves are going to go find it from these various data

sources.

(Noy. 28, 2023 Tr. at T148:7-18). When Counsel asked a question about “any

additional opinions with respect Ms. Craft’s opinions with regard to the

identification of potential TPP and consumer class members, Dr. Hughes responded

that “for TPPs, there is not a reliable methodology for identifying the implicit

exclusions. The ASOs, the TPAs, and the fully-insured health plans, there’s not a

reliable way of identifying them.” (/d at T148:19~25). He elaborated further,

stating: “Again, I don’t believe she has a methodology. She says the data are there

that would allow them to be identified. But she has not had—she has not laid out a

methodology with steps as to how she would do that to identify those entities.” □□□

at T150:15--22),

Dr. Hughes confirmed that he could not identify whether a plan was fully

insured from PBM data, but he was sometimes able to identify ASOs and TPAs from

the PBM data. (/d. at T150:23-T151:2). He indicated that this was shown by an

ASO column of the data. (Ud. at T151:3-4). Dr. Hughes commented that this

32

column would not always tell him who the ultimate payor is, and that that

information could be obtained through TPP data. Dr. Hughes stated: “otherwise, it’s

just individualized inquiry.” (Ud. at’T151:5—11).

When questioned about what sort of inquiry would be required for the

identification of the ultimate payor, Dr. Hughes stated: “So then you have got to go

to another data source, match it up, and see who the ultimate—the ultimate payor is.

But that’s not generally going to be in the PBM data, at least not consistently.” □□□□

at T151:12~20). And, when Dr. Hughes was asked what data source might have

such information, Dr. Hughes responded: “Well, the ASOs and TPAs themselves

would have it. The TPPs themselves would have it... . [T]hat would be the two

main sources of those data.” Ud. at T151:21-24).

In addition to these alleged problems, Dr. Hughes’ skepticism came down to

two other points. First, Dr. Hughes expressed his doubts that this data could be

presented cohesively. When questioned on Ms. Craft’s proposed methodology as to

whether different data could be harmonized, Dr. Hughes stated:

Maybe, maybe not. There’s ways that, using patient I.D.s, that the data

sets can be harmonized. But even then, you can’t always follow the

consumers over time if they change plans or their plan changes PBMs.

But again, in my experience, it’s— and I’ve done a lot of data merging

in my day, is that even if you have a patient number, it’s not

straightforward to just merge them together, it’s actually a really big

job. And you have to check your work, And you usually have to go in

and make some adjustments by hand because of typos or omissions in

the data.

33

Ud. at T150:2-8). Also, Dr. Hughes brought up issues with the data’s age. Dr.

Hughes noted:

And keep in mind that we’re going ten years back in the past and asking

consumers to be able to accurately attest to those things on the left. To

the consumer list, I would also add the patient number from their old—

from whatever health plan they had ten years ago. Do they have that?

Can they find that? What happens if it comes in and its blank, the

patient number is blank? Then she can’t merge it with anything. And

so the affidavit would be quite useless.

Ud. at T154:18-T155:1).

iii. Application

EPPs’ Motion for Class Certification fails to show that there is an

administratively feasible mechanism for identifying whether class members fall

within the class definition. Although Ms. Craft’s report and testimony purport to

establish that an administratively feasible mechanism exists for determining class

membership, the Court does not agree.

To satisfy the ascertainability requirement, a plaintiff must present a

methodology to identify class members and prove by a preponderance of the

evidence that such methodology will not require extensive and individualized

inquiry or mini-trials. See Marcus, 687 F.3d at 593. This ascertainability

requirement is two-fold: “(1) the class is defined with reference to objective criteria;

and (2) there is a reliable and administratively feasible mechanism for determining

34

whether putative class members fall within the class definition.” 974 F.3d at 469—

70 (internal citations omitted). The Court discusses both requirements below.

A. Objective Criteria

At oral argument, Ms. Craft testified to the following when presented with the

class definitions. With regard to the TPP class definition, Ms. Craft stated that it is

“objective precisely because each of these criteria can be answered with a simple

yes orno.... These are yes or no questions.” (Nov. 28, 2023 Tr. at T34:14-16).

She then evaluated the actual questions. Ms, Craft stated:

They include: Did you pay for one of these drugs? Yes or no? Did you

pay for it during the class period? Yes or no? Did you pay for it as a

result of a transaction within one of the class states? Yes or no? Did you

do so on behalf of a member employee insured participant or

beneficiary for whom you are committed to provide benefits? Those are

yeses and nos. These are objective, there’s nothing vague or gray about

that.

(id. at 134:12-23). Similarly, with respect to the Consumer Class, Ms. Craft stated

that it “has the same kind of yes or no questions as we talked about for the TPPs.”

(Nov. 28, 2023 Tr. at T48:21-T49:1).

The data and information presented is sufficient to satisfy the objective criteria

prong of the ascertainability requirement. With respect to TPPs, the class definition

is based on yes-or-no questions in a claims form, such as whether (1) an individual

paid for (2) branded Lipitor or generic atorvastatin calcium (3) during one of two

class periods (4) as a result of a prescription drug transaction that occurred within

35

one of the Class States and (5) if between June 28, 2011 and November 29, 2011,

without using a Pfizer co-pay card. (See Nov. 28, 2023 Tr. at T48:21-T49:1; Craft

Rep. at 94). Similarly, with respect to the Consumer Class, the criteria is based on

yes-or-no questions in a claims form, such as whether (1) an individual paid for (2)

branded Lipitor or generic atorvastatin calcium (3) during one of two class periods

(4) as a result of a prescription drug transaction that occurred within one of the Class

States and (5) if between June 28, 2011 and November 29, 2011, without using a

Pfizer co-pay card. (See Nov. 28, 2023 Tr. at T48:21--T49:1; Craft Rep. at □ 4).

The evidence sufficiently supports the conclusion that the class definitions are

adequately defined with reference to objective criteria, and Defendant does not argue

otherwise. Rather, they focus their argument on the next ascertainability

requirement: whether there is a reliable and administratively feasible mechanism for

determining whether class members fall within the class definition.

B. Reliable and Administratively Feasible Mechanisnt for Class

Identification

EPPs’ ascertainability problem lies within their inability to provide a reliable

and administratively feasible method to identify the class. To satisfy the

ascertainability requirement, a plaintiff must present a methodology to identify class

members and prove by a preponderance of the evidence that such methodology will

not require extensive and individualized inquiry or mini-trials. See Marcus, 687

36

F.3d at 593. Importantly, identification in this context does not mean that “a plaintiff

must be able to identify all class members at class certification—instead, a plaintiff

need only show that ‘class members can be identified.” Byrd, 784 F.3d at 163

(quoting Carrera, 727 F.3d at 308 n.2), EPPs are unable to do so.

Ms. Craft’s report delves into great detail regarding the availability of data,

citing a variety of data sources. She includes data from the named plaintiffs and

PBMs. Further, she explains that federal law, including HIPAA, mandates the

creation, retention and standardization of pharmaceutical transaction data. That data

includes information surrounding the precise drug sold, the dispensing date, the

patient’s identity, the insurer or its appointed administrator, the location of the

transaction and the product’s cost and how much that cost was allocated between the

consumer and any TPP. She further states that the data is recorded and maintained

by multiple entities, including the pharmacies that dispense the medication and the

PBMs. As such, she notes that there are multiple data sources that reflect class

member prescription drugs transactions. (Craft Reply Rep. at § 3). However, the

following question arises: how can EPPs show that the “methodology” put forth by

Ms. Craft allows EPPs to manipulate the data and identify and exclude potential

class members appropriately? In other words, how can EPPs show that class

members can be identified?

37

At oral argument on November 28, 2023, Ms. Craft testified that the available

data could be input into a computer program that, in turn, analyzes the data and

identifies the claims that fall within the class definition. (Nov. 28, 2023 Tr. at

T57:18-T58:5). In her report, Ms. Craft states that this process does not require

individualized fact-finding. (Craft Reply Rep. at 13; 22-24; 34). Ms. Craft

provided specific examples to explain that the data contains the information needed

to identify class members. (F.g., Craft Rep. at {[ 60-80; Craft Reply Rep. at □□□□

20). Ms. Craft discussed how membership can be verified using lists provided to the

Court and can “be enforced through certifications on claims forms” that are

submitted as part of a claims administration process; ultimately, Ms. Craft concludes

that class membership can be confirmed from multiple sources. (Craft Rep. at 19).

Further, when asked specifically about her methodology at oral argument, Ms. Craft

stated the following:

the data is to be produced containing all of the essential fields to

determine class membership. That data can then be, literally by a

computer, analyzed to identify any claims that do not comply with the

basic conditions of the class definition. And if there are any that do not

comply, the claims administrator has, in my proposed methodology, the

contact information to follow up with the entity that was submitting it,

to see if further information is necessary or to resolve any issue.

(Nov. 28, 2023 Tr. at T57:18-T58:5).

Based upon the Court’s review of the evidence, there is no methodology

proposed that is specific to the case. Stating that data has been produced and can be

38

analyzed by a computer to determine where it complies with the conditions of the

class definition is insufficient. The Court follows other courts who have decided

similarly on this issue. For example, in Ja re Niaspan Antitrust Litig., 464 F. Supp.

3d 678, 705 (E.D. Pa. 2020) (hereinafter, “Niaspan I’), the Niaspan J court examined

class certification in a case involving a reverse payment settlement agreement

concerning a lipid disorder medication, Niaspan. The Niaspan I court denied EPPs’

motion for class certification on various grounds, including ascertainability. Ms,

Craft, the ascertainability expert in that case, provided “a six-step methodology for

identifying class members based on her experience manipulating pharmaceutical

data, and that her methodology [was] particularly well-suited for the pharmaceutical

industry, ‘which are tracked, monitored, and recorded across a set of substantially

uniform variables.’” Niaspan I, 464 F. Supp. 3d at 704-05. Even with this step-by-

step method, the Niaspan I court denied class certification on the grounds that Ms.

Craft’s methodology—*that ‘OnPoint would be able to merge the data from the

vatious sources, identify and eliminate data errors, transform the data to standardize

the fields, eliminate duplicates, and compile a list reflecting the identities of the class

members contained in the data,’”——“f[did] not offer a methodology specific to this

case,” Jd, at 705 (internal marks and citations omitted). Indeed, the Niaspan J court

noted that “plaintiffs must provide more than Craft’s ipse dixit to prevail under a

rigorous ascertainability analysis.” Niaspan I, 464 F. Supp. 3d at 705.

39

Likewise, Ms. Craft’s methodology here is not specific to the case; stating that

data can be inputted into a computer which will cull the data does not offer a

methodology specific to this case nor does it provide the Court with more than ipse

dixit to determine its reliability. Further, EPPs’ methodology proposed here is much

weaker methodology than that presented in Niaspan I, although the methodology

was described in various ways throughout Ms. Craft’s Report, EPPs have pointed to

a general process that is comprised of a computer program analyzing data and sifting

through the same to determine inclusions and exclusions. While the Court does not

doubt that such a method could create a feasible methodology in the appropriate

case, the lack of specificity here with this process and the general discussion of a

“computer program” undertaking this work is insufficiently specific to this case—

particularly where the class is so vast. Such a methodology also does not present the

Court or Defendant with an approach that can be critically evaluated. Indeed, like

in Niaspan I, the lack of evidentiary support for this methodology fails to provide

Defendant with the opportunity to meaningfully test or respond to the assumptions

underlying EPPs’ Motion for Class Certification. .

Even if the methodology were specific to the case, there is no indication that

that methodology could reliably identify class members. Ms. Craft’s proposed

methodology is vague; it fails to provide any concrete procedure or step-by-step

methodology by which the Court could define the class, manipulate or analyze the

40

data, apply exclusions, and generate a list of class members that is not fact-intensive.

Stating that data can be analyzed by computer software which determines whether

someone is a class member is insufficient to satisfy the rigorous analysis of the class

certification process since Ms. Craft’s statements and conclusions amount to nothing

more than representations that class membership could be determined from data that

exists and is available. As the Court has noted throughout its analysis, neither Ms.

Craft nor EPPs have presented the actual data underlying many of her assumptions;

instead, she relies on the say so of PBM executives’ Declarations. Take for example

ASO and TPA data. Two Declarations on which EPPs heavily rely—Caremark and

Prime—state that Caremark and Prime possess data that can identify ASOs and

TPAs or the ultimate end-payor transactions. (Nov. 28, 2023 Tr. at T70:5-16; see

also Craft Rep. at 437). There has been no evidence provided to the Court that the

PBMs would be able to produce such data, and EPPs have not been able to

corroborate the PBM executives’ statements. The only evidence provided to the

Court is the Declarations presented by these two PBM executives. This does not

hold up against the rigorous analysis required of the class certification process—

especially since Defendant has not been afforded the opportunity to substantively

respond to these statements.

° See discussion at supra 12-13.

4]

Putting aside these concerns with the methodology, the reliability of the

methodology is also in question. Even assuming that the data exists, there is no

indication that it could be manipulated in a feasible manner to make the identification

of class members possible. As Dr. Hughes pointed out, EPPs have failed to provide

the Court with evidence that the data provided from multiple different entities could

be harmonized. As Dr. Hughes stated, the data harmonization process is “not

straightforward” and you cannot “just merge [data] together[;] it’s actually a really

big job. And you have to check your work. And you usually have to go in and make

some adjustments by hand because of typos or omissions in the data.” (Nov. 28,

2023 Tr. at T150:1-8), EPPs have provided no information as to how this process

would be undertaken, how long it would take, or how much it would cost—only

opining that this process has been done or could be done. Such statements are

insufficient to show the reliability of the data in question. The Court is also

concerned regarding data harmonization given the age of the records in question,

Here too EPPs have insufficiently addressed the problems implicated by the data’s

age and how such data could be reliable. See Vista Healthplan, Ine. v. Cephaton,

Inc., No. 2:06-cv-1833, 2015 WL 3623005, at *10 (E.D. Pa. June 10, 2015) (stating

how, while one consumer’s prescription history dating back to 2006 had been

presented to the Vista Healthplan court, the record contained “no evidence that other

pharmacies kept reliable records of this same type of patient data over that time

42

period” and how “it is insufficient to simply make assurances that records are readily

available without providing evidence that ‘retailer records in this case can be used

to identify class members.’” (quoting Carrera, 727 F.3d at 308)). Overall, even

though at class certification, Plaintiffs “need only show that ‘class members can be

identified[,]’” Plaintiffs have not made such a showing with the evidence presented.

Byrd, 784 F.3d at 163 (internal citations omitted) (emphasis in the original).

Additionally, EPPs’ methodology does not provide an administratively

feasible manner of ascertaining exclusions. That is, there is no manner presented

where EPPs will identify exclusions in a manner that is not fact-intensive, requiring

individualized fact-finding. This task is rendered difficult—particularly where EPPs

propose to certify two classes with more than nine explicit exclusions. Using the

rigorous analysis required under the class certification process, the Court finds this

process to be fact-intensive, requiring individualized fact-finding. The exclusions

relevant to the two different classes is discussed below.

a) ‘TPP Class Exclusions

EPPs principally argue that the proposed methodology fails to provide an

administratively feasible manner of identifying federal and state government entities

and ASOs, TPAs, and fully-insured health plans. These issues are discussed below.

With respect to Ms. Craft’s methodology for excluding state and federal

government entities, Ms. Craft provides three ways by which these entities can be

43

excluded. First, Ms. Craft states that PBMs can identify, flag, or exclude

transactions involving a federal or state government payor. (Craft Rep. at | 86).

Second, she states that a list of federal and state government payors can be cross-

checked against the names in the data and in the claim forms that are submitted. (/d.

at 89). Third, she claims that claimants can be required to verify via a certification

on the claim form that they are not a state or government payor. Ud. at J] 85-90).

Defendant argues that this reliance on affidavits to confirm that a payor is not a state

or government entity “amounts to nothing more than the alleged class member’s ‘say

so’ that they fall within the class and are not excluded[,]” further noting that the

evidence presented is insufficient to show that these entities could be excluded.

(ECF No. 1259 at 29).

The Court first notes the similarities between EPPs’ methodology here and

that put forth in In re Niaspan Antitrust Litig., 555 F. Supp. 3d 155, 165 (B.D. Pa.

2021), aff'd, 67 F.4th 118 Gd Cir. 2023) (hereinafter, “Niaspan IP’), The Niaspan

I court evaluated the same exclusion applicable to EPPs. EPPs in that case posited

that these exclusions could be identified by:

(1) PBMs can identify and exclude federal and state government-

funded plans prior to producing data; (2) Managed Markets Insight &

Technology (“MMIT”) data can be used to identify and exclude federal

and state government-funded plans; (3) the 31 class states can provide

a historical list of their state-funded plans; and (4) Milliman, Inc.

44

(“Milliman”) data can be used to identify and exclude state-funded

plans.

Id. at 164. The Niaspan iI court farther noted that Ms. Craft “claim[ed] that PBMs’

websites ‘confirm ... that they have the capability to tailor their programs to the

needs of federal and state government entities and [PBMs] aggressively market this

capability.’” Jd. The Niaspan II court noted that Ms. Craft also stated that “PBMs

‘must... maintain... data that identifies government payors’ because ‘[fjederal

law prohibits enrollees in federal health programs from participating in pharmacy

incentive programs.’” Jd. Overall, the Niaspan H court found that this evidence

sufficiently showed that EPPs presented an administratively feasible mechanism for

excluding federal and state government plans from the proposed class. See Niaspan

H, 555 F. Supp. 3d at 164-65. The Niaspan I methodology is similar to that put

forward by EPPs here. Indeed, EPPs have proposed to use PBM data and data from

third parties Milliman, the NCSL or State Offices of Personnel to identify and

exclude government entities. (Craft Rep. at JJ 89-90; see also Nov. 28, 2023 Tr. at

T42:18-T47:8). Also similar to Niaspan I, EPPs have put forth a manner of culling

down the data, stating the manner in which state government data can be narrowed.

Specifically, Ms. Craft notes that these third-party sources allow for the data to be

culled and grouped into different categories by state, thus narrowing the relevant

states by which a claims administrator would analyze the data. Ms. Craft also notes

in this case that “[mJajor PBMs . .. aggressively market their specialized skills in

45

handling federal and state government plans to assure regulatory compliances[]” on

their websites. (Craft Rep. at § 87). Further, Ms. Craft notes that “[flederal law

prohibits enrollees in federal health programs from participating in pharmacy

incentive programs[,]” thereby meaning that pharmacies and PBMs “must identify

which prescriptions are reimbursed under federal and state programs.” (Craft Rep.

at § 88). These similarities noted, Niaspan LI is distinguishable given the notable

difference with EPPs’ methodology here.

The main problem rests again with EPPs’ reliance on PBM data to confirm

the exclusions. Per their own documents, the PBMs on which EPPs rely for

providing the information about the federal and government entities—CVS

Caremark, Express Scripts, MedImpact and Optum RX—account for (at most)

around 40 percent of the market share of annual prescription volume in the relevant

time period, (Craft Rep. at Table 2; see also ECF No. 1259 at 29). How EPPs can

show that the federal and state entities unaccounted for in the PBM data they do

have—either in the other roughly sixty percent of PBM data or by supplementing

that data with the Milliman data, NCSL, State Offices of Personnel data, or historical

state data lists—seems highly speculative and to the Court, amounts to relying on

“say so” representations by EPPs. These “say so” representations may require

individualized inquiry. Further, even assuming that federal law does mandate the

retention of federal health program enrollee information by pharmacies and PBMs,

A6

there is no evidence provided to the Court that the other data sources that are not

pharmacies or PBMs on which Ms. Craft relies could fill this gap. These issues—

not present in Niaspan I7/—render Ms. Craft’s methodology for excluding these

federal and state governmental entities unreliable. And, without more evidence,

such a methodology does not pass the rigorous analysis required of the Court in the

class certification process.

EPPs’ proposed methodology to exclude fully-insured health plans, ASOs,

and TPAs also falls short. First, the Court notes its serious reservations with respect

to EPPs’ ability to identify ASOs. Take Ms. Craft’s reliance on Humana data to

identify ASOs. In support of the assertion that ASOs can be identified using Humana

data, Ms. Craft states that Humana uses two fields to identify the customer’s funding

status: an ASO indicator variable (‘ASO IND” field) and a line of business

description (“LOB DESC” field). (Craft Rep. at 4 47). Ms. Craft further states:

“The ASO_IND variable is populated with yes (“Y”) if the identified Customer is

self-funding [i.e., the end payor] and is an ASO client of Humana, or no (“N”) if the

plan is fully insured and the identified insurer is therefore the TPP.” (Ud.), However,

as Ranbaxy’s Counsel pointed out, when the “ASO_IND” field was set to “Y,” the

results in the “Customer Name” field identified Humana, which acts as both an ASO

and end payor. (Nov. 28, 2023 Tr. at T71:1-T73:12). Thus, the data ultimately

provided by Humana when manipulated could not accurately identify the ultimate

47

end payor as Ms. Craft had opined. In addition and as the Court has previously

stated, two Declarations on which EPPs heavily rely-—those of Ms. Fridberg and Mr.

Devaney—state that Caremark and Prime possess data that can identify ASOs and

TPAs or the ultimate end-payor transactions. (Nov. 28, 2023 Tr. at T70:5~-16; see

also Craft Rep. at § 37). Yet this data has not been produced in this matter, and

there has been no evidence provided to the Court that the PBMs would be able to

produce such data. This amounts to nothing more than relying upon say so

representations from Declarations that something could be accomplished.

Even when looking at how Ms. Craft states that this exclusion could be

applied, Ms. Craft states that EPPs rely on the PBM executives’ representations

which state that the data they possess can confirm exclusions. Without having seen

this underlying data, Ms. Craft refers to Ms. Fridberg’s Declaration, which states

that “Prime maintains data indicating the name of the TPA or ASO and the self-

funded payor client of the ASO or TPA. Prime can associate these Carrier and

Account IDs with Carrier and Account Names.” (Fridberg Declaration at 4 16; Craft

Rep. at 7 37; Nov. 28, 2023 Tr. [62:16—1T62:9). Neither Ms. Craft nor Ms. Fridberg

describe how Carrier and Account IDs will be associated with Carrier and Account

Names from the Prime data. (/d.). When evaluating this situation under the rigorous

analysis required by the class certification process, the Court is unconvinced by

EPPs’ approach to excluding fully-insured health plans, ASOs, and TPAs given that

48

there has been little to no data produced on this point to support such an assertion,

The Court believes this approach to exclusions amounts to nothing more than relying

on EPPs’ “say so” representations.

Moreover, as Dr. Hughes points out, the data to be retrieved is ten years old.

The data’s age implicates several issues such as the fact that the data may be

incomplete or corrupted due to its age and changes in technology. Ms. Craft does

not provide an opinion on this issue, and EPPs offer no evidence as to how this aged

data can be used to identify and exclude fully-insured health plans, TPAs, and ASOs.

“[A]ssurances that a party ‘intends or plans to meet the requirements’ are insufficient

to satisfy Rule 23.” Carrera, 727 F.3d at 311 (internal citations omitted), Without

more, the Court cannot determine whether fully-insured health plans, TPAs, and

ASOs can be identified and excluded from the class in an administratively feasible

manner.

This brings the Court to another concern: even assuming that ASOs, TAs,

and fully-insured health plans could be identified through data produced by PBMs

or from claim forms, the Court is unconvinced that these additional steps can be done

in (1) an administratively feasible manner that (2) would not necessitate mini-trials

or individualized fact-finding. This is because of the actual status of the insurance

company with reference to its consumer, Often, an insurance company may be an

ASO for some customers and a TPP for others. The Court’s understanding is that a

49

claim form’ will provide a question about the insurance company’s status. This

answer will be verified through the PBM data. As demonstrated from the above

discussion, however, PBM data may be insufficient. Accordingly, the verification

of status requires a review of the underlying documents between the insurance

company and the client—a process that requires extensive and individualized fact-

finding and follow-ups.

After having carefully reviewed the record and the evidence set forth by EPPs

under the rigorous analysis required in the class certification process, the Court

concludes that EPPs have not carried their burden by a preponderance of the

evidence in setting forth a reliable and administratively feasible mechanism for

identifying and excluding federal and state government entities; fully-insured health

plans; TPAs; and ASOs. See in re Wellbutrin XL Antitrust Litigation, 308 F.R.D.

134, 150 (ELD. Pa. 2015).

b) Consumer Class Exclusions

As for the Consumer Class, Ranbaxy takes issue with EPPs’ method for

ascertaining members in the Total Generic Exclusion Period (June 28, 2011 through

November 29, 2011) who used Pfizer Co-Pay Cards and for excluding “flat co-pay”

7 See supra at 15-19.

50

consumers who purchased Lipitor via a fixed dollar copayment that did not vary

based on the drug’s status as a brand or generic. These issues are discussed below.

The Total Generic Exclusion Period within the Consumer Class definition

excludes individuals who used Pfizer co-pay cards to pay for purchases of Lipitor.

EPPs admit the Pfizer co-pay cards have not been reviewed, but EPPs purport to

exclude this category of consumers based on Ms. Craft’s conclusion that Pfizer kept

records of the Pfizer Co-Pay Card Program; this assertion is based upon a Pfizer

PowerPoint presentation on the co-pay cards which discussed the redemptions by

patients and showed percentages of redemption among different tiers of costs. (ECF

No. 1252 at 31).

EPPs present the Court with no methodology regarding the Pfizer Co-Pay

Card exclusion. Although this co-pay card selection may be easily adduced, the

Court does not accept EPPs’ representations—which Ms. Craft has not seen or

reviewed-—that such data is available in such a manner that it can be used in an

administratively feasible way to determine class membership of consumers falling

within the Total Generic Exclusion Period. Without having reviewed—let alone

seen—the Pfizer co-pay data, EPPs’ contentions and Ms. Craft’s conclusion are not

corroborated. Moreover, Dr. Hughes explains that co-pay cards are issued in

different amounts and, depending on the amount of the benefit, a co-pay card

individual may or may not have an antitrust injury. Dr. Hughes claims the amount

51

of the injury, if any, may be determined by individually reviewing co-pay card

documents. (Nov. 28, 2023 at T147:1-14). This would result in further

individualized inquiry. The Court is unable to evaluate these statements based upon

the information before it.

The Court also has concerns regarding the EPPs’ exclusion for flat co-pay

consumers within the Consumer Class definition. EPPs define this group of

excluded consumers as those “who purchased Lipitor only via a fixed dollar

copayment that does not vary on the basis of the drug’s status as brand or generic.”

(ECF No. 1252 at 12). EPPs provide three ways to identify this exclusion. First,

EPPs propose to compare the brand and generic co-pay amounts from the data to

determine if they are identical. (Craft Rep. at { 98). Second, EPPs state that

consumers can verify, through claim form affidavits, that consumers did not make a

purchase using a flat co-pay plan. (/d.). Third, EPPs state that PBMs can flag plans

that utilize a flat co-pay structure. (E.g., Craft Rep. at | 100; Devaney Declaration

at 7 9). EPPs note in a footnote that flat co-pay plans cannot be excluded because

the TPP portion of the payment qualifies for the TPP class. (ECF No. 1252 at 34

n.18).

Ms. Craft relies upon a 2011 survey by the Kaiser Family Foundation and

Health Research & Educational Trust to conclude that there would be no, or few

consumers whose claims would be subject to the same flat co-pay for the brand and

52

generic drugs, (See Craft Rep. at § 99). While PBM declarants state that that claims

data can generally be sorted to exclude members of plans having single-tier flat co-

pay structure, EPPs in their brief concede that PBMs would only be able to flag the

plans that apply a flat co-pay structure because TPPs qualifying for class

membership would be included in those flagged plans. (ECF No. 1252 at 34 n.18).

Another concern facing the Court is EPPs’ failure to address how, in light of Pfizer’s

brand continuity program, a brand Lipitor purchaser could have paid the same co-

pay as a generic—even if the brand did not have a flat co-pay structure. (See ECF

No, 1326-3 at 12). As such, the only method to reliably identify flat co-pay

consumers would be through an individualized analysis of the consumer’s health

plan documents and the say so of the claims forms submitted; EPPs have failed to

address how flat co-pay plans would then be removed from the resulting list without

excluding qualifying TPPs.

The Court finds the Jn re Wellbutrin XL Antitrust Litigation, 308 F.R.D. 134

(E.D. Pa. 2015) decision instructive here. There, the court granted a motion to

decertify an indirect purchaser class for several reasons, including that the class

failed to show how records from various disparate entities could be synthesized to

ascertain the consumers that paid a flat co-payment for the purchase of the brand

drug and generic drug. /d. at 150. Instead, the Jn re Wellbutrin court found that the

class’s evidence on ascertainability “barely [went] further than repeated assurances

53

that showing ascertainability in a pharmaceutical case is not difficult and there are

extensive purchase records in the pharmaceutical industry that could be used... .”

Id. In a similar way, EPPs through their expert have provided the Court with mere

assurances that—based on extensive records available—a reliable and

administratively feasible mechanism exists to sift through the TPPs who applied a

flat co-pay structure. The Court has no way of knowing how this could be done, and

with the information before it, it appears that to exclude flat co-pay consumers would

require an individualized fact-finding inquiry and a review of the TPP data. This

process would be burdensome, fact-intensive, and like a mini-trial. EPPs’ highly

individualized information and “ad hoc approach ... does not adequately establish

a feasible methodology to address the many class exclusions.” Niaspan I, 464 F.

Supp. 3d at 705.

After having reviewed the record and the evidence set forth by EPPs with the

rigorous analysis required of the Court, the Court concludes that EPPs have not

carried their burden by a preponderance of the evidence in setting forth a reliable

and administratively feasible mechanism for identifying and excluding members of

the Consumer Class. See In re Wellbutrin XL Antitrust Litigation, 308 F.R.D. 134,

150 (E.D. Pa. 2015).

c) Costs

54

As a final note, the Court notes that neither EPPs nor their expert have

discussed or offered an opinion as to the costs involved with implementing their

proposed methodology. At oral argument on November 28, 2023, Ms. Craft

admitted that she “wasn’t asked to offer an opinion” about the cost of her proposed

ascertainability methodology, though she added that the process would be “very

inexpensive.” (Nov. 28, 2023 Tr. at [76:1-13). The cost factor is relevant to the

determination of whether the proposed methodology can be implemented in an

administratively feasible manner, and the Court does not believe that the record

supports that the ascertainability methodology can be.

In particular, the Court harbors concerns regarding the cost of the claims

administration process; related to the fact-intensive and time-involved nature of

reviewing available data is the cost of this process. At its broadest, the claims

administration process is comprised of a claimant submitting identification and proof

of loss to the class administrator. Then, the claims administrator will review and

verify the loss and the claims form. Ifthe claims administrator questions the loss on

the claims form, then he or she will contact the claimant. Once the claims

administrator accepts the claim, the completed claim will be transmitted to Ms.

Craft’s firm for processing. Yet even in this process, the claims administrator may

follow-up with the claimant several times. There is no estimate as to how many

follow-ups may be necessary or how much this initial claims process would cost.

55

Moreover, EPPs have not explained whether the costs associated with the claims

administrator rise as the number of individual contacts with claimants rises. If there

are many contacts with many individual claimants, the cost here could be substantial.

While the EPPs need not lay out these costs with certainty, they must provide

the Court with more information than it will be “very inexpensive.” This does not

meet the burden of proof. The Court harbors significant doubt that this methodology

would be cost effective—especially when looking at other antitrust cases presented

with similar methodologies. See Niaspan I, 464 F. Supp. 3d 678 at 707 (noting how,

while even “if identification of class members [were] technically possible, EPPs’

proposed methodology [might] be prohibitively expensive and thus infeasible” when

looking at similar pay-for-delay cases).

Til.

Given that the Court has previously ruled that EPPs cannot prove an essential

element of their action, causation, this Motion is accordingly denied, (See ECF No.

1415).

However, even if the Court had not granted summary judgment on causation,

class certification would still be denied. This is because EPPs have failed to show

by a preponderance of the evidence that there is a reliable and administratively

56

feasible mechanism for determining whether class members fall within the class

definition as required under Rule 23(b)(3). °

( un

PETER G. SHERIDAN, U.S.D.J.

§ Without analyzing Dr. Hal Singer’s report, his use of average cost per pill on an

overall basis as a measure of determining damages where there are rebates to specific

TPPs requires further discussion.

57