[Federal Register Volume 88, Number 83 (Monday, May 1, 2023)]
[Notices]
[Pages 26552-26559]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-09183]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Food and Drug Administration

[Docket No. FDA-2022-N-0081]


Agency Information Collection Activities; Submission for Office 
of Management and Budget Review; Comment Request; Tradeoff Analysis of 
Prescription Drug Product Claims in Direct-to-Consumer and Healthcare 
Provider Promotion

AGENCY: Food and Drug Administration, HHS.

ACTION: Notice.

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SUMMARY: The Food and Drug Administration (FDA) is announcing that a 
proposed collection of information has been submitted to the

[[Page 26553]]

Office of Management and Budget (OMB) for review and clearance under 
the Paperwork Reduction Act of 1995.

DATES: Submit written comments (including recommendations) on the 
collection of information by May 31, 2023.

ADDRESSES: To ensure that comments on the information collection are 
received, OMB recommends that written comments be submitted to https://www.reginfo.gov/public/do/PRAMain. Find this particular information 
collection by selecting ``Currently under Review--Open for Public 
Comments'' or by using the search function. The title of this 
information collection is ``Tradeoff Analysis of Prescription Drug 
Product Claims in Direct-to-Consumer and Healthcare Provider 
Promotion.'' Also include the FDA docket number found in brackets in 
the heading of this document.

FOR FURTHER INFORMATION CONTACT: Jonna Capezzuto, Office of Operations, 
Food and Drug Administration, Three White Flint North, 10A-12M, 11601 
Landsdown St., North Bethesda, MD 20852, 301-796-3794, 
[email protected].
    For copies of the questionnaire, contact: Office of Prescription 
Drug Promotion (OPDP) Research Team, [email protected].

SUPPLEMENTARY INFORMATION: In compliance with 44 U.S.C. 3507, FDA has 
submitted the following proposed collection of information to OMB for 
review and clearance.

Tradeoff Analysis of Prescription Drug Product Claims in Direct-to-
Consumer and Healthcare Provider Promotion

OMB Control Number 0910-NEW

I. Background

    Section 1701(a)(4) of the Public Health Service Act (42 U.S.C. 
300u(a)(4)) authorizes FDA to conduct research relating to health 
information. Section 1003(d)(2)(C) of the Federal Food, Drug, and 
Cosmetic Act (FD&C Act) (21 U.S.C. 393(d)(2)(C)) authorizes FDA to 
conduct research relating to drugs and other FDA-regulated products in 
carrying out the provisions of the FD&C Act.
    The OPDP's mission is to protect the public health by helping to 
ensure that prescription drug promotion is truthful, balanced, and 
accurately communicated. OPDP's research program provides scientific 
evidence to help ensure that our policies related to prescription drug 
promotion will have the greatest benefit to public health. Toward that 
end, we have consistently conducted research to evaluate the aspects of 
prescription drug promotion that are most central to our mission. Our 
research focuses in particular on three main topic areas: (1) 
advertising features, including content and format; (2) target 
populations; and (3) research quality. Through the evaluation of 
advertising features, we assess how elements such as graphics, format, 
and disease and product characteristics impact the communication and 
understanding of prescription drug risks and benefits. Focusing on 
target populations allows us to evaluate how understanding of 
prescription drug risks and benefits may vary as a function of 
audience, and our focus on research quality aims at maximizing the 
quality of research data through analytical methodology development and 
investigation of sampling and response issues. This study will inform 
the first and second topic areas, advertising features and target 
populations.
    Because we recognize that the strength of data and the confidence 
in the robust nature of the findings are improved by using the results 
of multiple converging studies, we continue to develop evidence to 
inform our thinking. We evaluate the results from our studies within 
the broader context of research and findings from other sources, and 
this larger body of knowledge collectively informs our policies as well 
as our research program. Our research is documented on our home page, 
which can be found at: https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/office-prescription-drug-promotion-opdp-research. The website includes links to the latest Federal Register 
notices and peer-reviewed publications produced by our office.
    The proposed research examines the relative importance of 
prescription drug product information such as prescription drug 
efficacy, risk, adherence, and patient preference claims in two medical 
conditions (type 2 diabetes and psoriasis) in consumer and physician 
samples. When confronted with an important decision, people tend to 
make choices that reflect a series of tradeoffs between certain 
desirable and undesirable attributes of a product, service, or 
experience. Pharmaceutical manufacturers provide information about 
prescription drug products, including side effects, contraindications, 
and effectiveness, through product labeling and promotional materials 
(21 CFR 202.1(e)). The treatment choices of diagnosed consumers and 
treating physicians have been shown to be influenced by certain 
characteristics, such as the drug's perceived impact on quality of 
life, complexity of dosage regimens, mode of administration, cost to 
family and self, and marketing claims unrelated to medicinal properties 
(Refs. 1 to 5). Although diagnosed consumers may weigh the risks, 
benefits, or other salient characteristics of prescription drug 
products differently than physicians, little research directly compares 
the treatment preferences of these two groups (Ref. 6). Understanding 
the tradeoffs among drug product characteristics diagnosed consumers 
make--and how the tradeoffs could potentially differ from the tradeoffs 
made by physicians--will provide valuable insight into the relevance 
and impact of various product attributes and promotional claims on 
informed choices and treatment decisions.
    We intend to examine these tradeoffs using a choice-based conjoint 
analysis, also known as a discrete choice experiment. Conjoint analysis 
is a broad class of survey-based techniques used to estimate how 
attractive or influential different features of choice options or 
product attributes are in determining purchase behavior or treatment 
choices (Ref. 7). Conjoint analysis can be used to examine the joint 
effects and tradeoffs of multiple variables or product attributes on 
decisions. A choice-based conjoint analysis is based on the principle 
that products are composed of a set of attributes, and each attribute 
can be described using a finite number of levels. In the proposed 
research, participants will be shown a carefully designed sequence of 
choice tasks containing up to five hypothetical product attributes--in 
this case, profiles describing fictitious prescription drug products 
for either type 2 diabetes or psoriasis. Using data from the choices 
that participants make across these tasks, we can use statistical 
techniques to draw inferences about the relative value they place on 
different product attributes, estimate the relative importance of 
different attributes, explore the tradeoffs that consumers and 
physicians are willing to make to avoid or accept specific attribute 
levels, and compare the preferences of these two groups (Ref. 8).
    We estimate that participation in the study will take approximately 
20 minutes. Adult participants aged 18 years or older will be recruited 
by email through an internet panel, and participant eligibility will be 
determined with a screener at the beginning of the online survey. The 
consumer sample will consist of adults who self-report as having been 
diagnosed by a healthcare provider with either psoriasis or type 2 
diabetes. For the consumer sample, we will exclude

[[Page 26554]]

individuals who work in healthcare settings because their knowledge and 
experiences may not reflect those of the average consumer. The 
physician sample will consist of primary care physicians and 
specialists who report treating patients with psoriasis or type 2 
diabetes. For the physician sample, we will exclude individuals who 
spend less than 50 percent of their time on direct patient care. 
Department of Health and Human Services employees and individuals who 
work in the marketing, advertising, or pharmaceutical industries will 
be excluded from both respondent groups. Respondents will receive a 
survey invitation with a unique password-protected link. All panel 
members are recruited following a double opt-in process. Sample sizes 
were estimated by combining approaches for conjoint analysis suggested 
by Orme (Ref. 9) and Johnson et al. (Ref. 10).
    The target sample size for the main study is 800 physicians and 800 
consumers, with half of each cohort focusing on treatments for 
psoriasis and the other half focusing on treatments for type 2 
diabetes. Prior to conducting the main study, we will conduct at least 
one pretest. If the first pretest reveals that changes to the 
measurement instruments, stimuli, or procedures are required, a second 
pretest will be conducted with revised materials. The target sample 
size for each wave of pretests is 60 physicians and 60 consumers.
    In the Federal Register of April 25, 2022 (87 FR 24313), FDA 
published a 60-day notice requesting public comment on the proposed 
collection of information. Two submissions (https://www.regulations.gov 
tracking numbers l3s-66ri-uyh2 and l2z-6w2l-mpk1) were outside the 
scope of the research and are not addressed further.
    FDA received eight comments that were PRA-related. Within those 
submissions, FDA received multiple comments that the Agency has 
addressed. For brevity, some public comments are paraphrased and 
therefore may not state the exact language used by the commenter. We 
assure the commenter that the entirety of their comments was considered 
even if not fully captured by our paraphrasing in this document. 
Comments and responses are numbered here for organizational purposes 
only.
    (Comment 1) Five comments expressed support for the study.
    (Response 1) We acknowledge and appreciate the support of this 
study.
    (Comment 2) One comment stated the collection of information is not 
necessary for the proper performance of FDA functions and questioned 
the practical utility of the study. Another comment asked for 
clarification about how the results would be applied to OPDP decision 
making. The first of these comments suggests that an alternate approach 
would be to dedicate resources to enforcing heavier penalties for 
misleading, incomplete, or false information.
    (Response 2) The OPDP's mission is to protect the public health by 
helping to ensure that prescription drug promotion is truthful, 
balanced, and accurately communicated. Understanding the tradeoffs 
among drug product characteristics diagnosed consumers make--and how 
the tradeoffs could potentially differ from the tradeoffs made by 
physicians--will provide OPDP valuable insight into the relevance and 
impact of various product attributes and promotional claims on informed 
choices and treatment decisions. Gaining a better understanding of what 
information has the most meaning and impact for audiences informs 
OPDP's approach to ensuring that promotional communications are 
truthful, balanced, and accurately communicated.
    (Comment 3) One comment expressed concern that results of the study 
possibly could inform potential guidance on patient-focused drug 
development.
    (Response 3) The purpose of this research is to examine the 
tradeoffs that consumers and physicians make when considering product 
claims that may appear in promotional communications. The fact that FDA 
is conducting research does not create any requirements.
    (Comment 4) One comment asked how adherence and patient preference 
claims would be included in drug product information, as the commenter 
does not believe there is currently a patient preference claim or 
adherence data in FDA-approved prescription drug information for any 
product in either of the two conditions proposed in this study.
    (Response 4) Prescription drug promotion often includes information 
beyond what is contained in the FDA-approved prescription information 
for the product. The attributes that make up the ``additional 
information about the drug'' are example marketing claims that have 
been used in product promotion. We will test reasonable scenarios based 
on realistic examples.
    (Comment 5) One comment suggested clarification of the sentence, 
``The treatment preferences of diagnosed consumers and treating 
physicians have been shown to be influenced by certain characteristics, 
such as the drug's perceived impact on quality of life, complexity of 
dosage regimens, mode of administration, cost to family and self, and 
marketing claims unrelated to medicinal properties (Refs. 1 to 5)'' (87 
FR 24313 at 24315). The comment asserts that it is inaccurate to state 
that ``preferences'' are influenced by the characteristics of 
alternatives, when it is actually ``choice'' that is a reflection of 
the characteristics or attributes.
    (Response 5) We have revised the sentence in question, as 
suggested, to make it clear that treatment choices are influenced by 
these example characteristics. The revised sentence reads, ``The 
treatment choices of diagnosed consumers and treating physicians have 
been shown to be influenced by certain characteristics, such as the 
drug's perceived impact on quality of life, complexity of dosage 
regimens, mode of administration, cost to family and self, and 
marketing claims unrelated to medicinal properties.''
    (Comment 6) Two comments asked for clarification on the guidelines 
that will be used to determine the attributes and levels in the 
experiment.
    (Response 6) We selected attributes and attribute levels based on 
information gathered through: (1) a systematic literature review of 
preference elicitation studies targeted toward prescription 
pharmacological treatments for psoriasis or type 2 diabetes among 
diagnosed consumers or healthcare providers (HCPs) reported in peer-
reviewed journal articles or book chapters published in English through 
the end of September 2020 and (2) semistructured, one-on-one interviews 
with physicians and diagnosed consumers conducted as part of the 
formative work for this project.
    The systematic literature review focused on research examining 
preferences for attributes and characteristics of prescription drug 
products indicated for psoriasis and type 2 diabetes. The review 
addressed two research questions with an emphasis on informing our 
choice of elicitation method for the main study and identifying 
characteristics of prescription drug products relating to risk, burden, 
adherence, and benefits that physicians and consumers who have been 
diagnosed with the target medical conditions consider when choosing 
among treatment options. After screening candidate articles against our 
eligibility criteria, we retained and extracted information from 30 
articles related to psoriasis and 28 articles for type 2 diabetes that 
informed our choice of attributes and levels. Our aim with the one-on-
one interviews was

[[Page 26555]]

to better understand how physicians and diagnosed consumers navigate 
decision making related to prescription drug products and to verify 
that attributes identified through the systematic literature review 
corresponded with the characteristics that physicians and consumers 
care about when making prescription drug choices. In all, we conducted 
35 interviews with physicians who treat psoriasis or type 2 diabetes 
and 70 interviews with consumers who self-reported that they have been 
diagnosed with one of the two chronic conditions (n = 35 per 
condition). We asked specific questions about attributes and attribute 
levels found in the literature review. We also used the interviews to 
elicit additional characteristics that may not have been represented in 
the literature.
    (Comment 7) One comment suggests use of an opt-out (i.e., decline 
therapy) or status quo (i.e., no change) option in the questionnaires.
    (Response 7) There can be benefits to including an ``opt-out'' or 
``status quo'' option in choice experiments, depending on the goals of 
the research. For example, if one is interested in estimating treatment 
uptake, the inclusion of an ``opt-out'' option may be helpful. However, 
estimating treatment uptake is not a goal of this study, and we believe 
the limitations of including an ``opt-out'' or ``status quo'' option 
outweigh the benefits in this instance. One limitation is the potential 
for satisficing--participants choosing the ``opt-out'' or ``status 
quo'' option because it requires less effort than reflecting on the 
option that best aligns with their preferences (Ref. 11). Additionally, 
in the context of this study, the status quo will differ among 
participants, raising the issue of how to interpret findings from 
diagnosed consumers who choose that option.
    (Comment 8) Two comments question the decision to employ a discrete 
choice experiment (DCE) method and the number of attributes chosen, 
with one comment noting that there are other methods that may allow for 
a higher number of attributes to be tested. One of the comments noted 
the existence of other DCE studies conducted in similar treatment 
populations and requested clarification about how this study would 
differ from prior research.
    (Response 8) One of the goals of the systematic literature review 
we conducted as part of the formative work for this study was to 
examine methods that have been used to elicit consumer or HCP 
preferences regarding treatment options for psoriasis and type 2 
diabetes. An overarching observation from the systematic literature 
review is that there is a gap in the literature for studies that 
directly compare treatment preferences of diagnosed consumers and HCPs. 
There is also a lack of studies that examine the relative importance of 
marketing claims versus other types of promotional claims. This study 
will help fill these gaps. A DCE was the most common methodology used 
in prior research, and it has clear advantages over other methods for 
the purposes of the proposed study. Perhaps the most relevant benefits 
of the method are the flexibility to efficiently estimate the overall 
utility of different treatment profiles, the relative importance of 
attributes, and the preference weights for specific attribute levels 
all within the same design (see Ref. 12 for an analysis that covers all 
three of these aspects). Moreover, tradeoffs that diagnosed consumers 
and HCPs are willing to make between attributes can be estimated from 
DCE data by calculating the marginal rate of substitution or the ratio 
of relative importance scores for pairs of attributes (Refs. 12 to 15).
    In designing the DCE for this project, we aim to conduct subgroup 
analyses comparing these research populations. Generally, this requires 
using the same attributes and levels for both research populations, 
though some degree of latitude is required to tailor the wording of 
background information, questions, and stimuli to match the target 
audience (e.g., plain language for consumers, medical terminology when 
appropriate for HCPs).
    For planning purposes and in order to establish target sample 
sizes, in the 60-day Federal Register notice for this study, we assumed 
a design with 5 attributes, 2 to 4 levels per attribute, 10 choice 
tasks per participant, and 2 options per task square. Our review 
revealed that these assumptions are well within the median design 
parameters used in prior studies.
    We will include methodological details concerning the experimental 
design in the report of results. Finally, while the comment did not 
identify any specific ongoing research as overlapping, we note that in 
general, in any event, OPDP may conduct concurrent or overlapping 
studies on similar topics.
    (Comment 9) One comment suggested use of an efficient design, 
including blocking, as a way to minimize the burden of collection on 
respondents.
    (Response 9) We intend to use an efficient design to reduce the 
number of choice tasks and have noted it as a burden reduction strategy 
in the information collection submission to OMB.
    (Comment 10) One comment asserted that internet panels are prone to 
selection bias and suggested the study address this potential 
limitation.
    (Response 10) Participants in the proposed studies will be 
convenience samples rather than probability-based samples of diagnosed 
consumers and physicians. The strength of the experimental design used 
in this study lies in its internal validity, on which meaningful 
estimates of differences across manipulated attributes can be produced 
and generalized. This is a counterpoint to observational survey 
methodologies, where estimating population parameters is the primary 
focus of statistical analysis. The recruitment procedures in this study 
are not intended to meet criteria used in survey sampling, where each 
unit in the sampling frame has an equal probability of being selected 
to participate. In a representative, observational survey study, 
response rates are often used as a proxy measure for survey quality, 
with lower response rates indicating poorer quality. Nonresponse bias 
analysis is also commonly used to determine the potential for 
nonresponse sampling error in survey estimates. However, concerns about 
sampling error do not generally apply to experimental designs, where 
the parameters of interest are under the control of the researcher--
rather than being pre-established characteristics of the participants. 
Participants will be recruited through online panels, which include a 
diverse range of participants in regard to age, race/ethnicity, income, 
education, and employment. We also have proposed the use of soft quotas 
to further ensure that we will recruit a diverse sample. See Response 
12 for a more detailed description of the panels to be used in this 
research.
    (Comment 11) Two comments questioned the Agency's methods for 
ensuring it is selecting patients as study participants.
    (Response 11) Our eligibility criteria involve a self-reported 
diagnosis of plaque psoriasis or type 2 diabetes, which appropriately 
reflects the audience for DTC promotion where a verified diagnosis is 
not a criterion. The screener includes a question (screening question 5 
(S5)) that asks whether a doctor, nurse, or other health professional 
has ever told the respondent they had at least one of seven health 
conditions. Participants who do not select plaque psoriasis or type 2 
diabetes will be flagged as ineligible for the study. The other 
conditions are included as response

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options to help disguise eligibility criteria from respondents as they 
complete the screener.
    (Comment 12) One comment stated it is unclear how physicians will 
be recruited, and one comment asserted that how consumers will be 
identified is not mentioned.
    (Response 12) For the pretests and main study, participants will be 
drawn from participant panels managed by Dynata. Dynata recruits panel 
members through a combination of email and online marketing and by 
invitation, with over 300 diverse online and offline affiliate partners 
and targeted website advertising. By using multiple recruitment 
methods, Dynata is able to recruit a diverse set of consumers and 
decision makers to participate in their panels and will ensure 
demographic diversity of participants' genders, ages, and education 
levels. Panel inclusion is by invitation only, and Dynata invites only 
pre-validated individuals with known characteristics to participate in 
the consumer panels. The physician sample for the pretest and main 
study will be drawn from Dynata's Healthcare Panel, which is a 
physician panel used exclusively for healthcare research. Dynata's 
Healthcare Panel uses a multimode approach that combines email, fax, 
and direct mail to recruit HCPs to participate in online surveys. 
Additionally, Dynata purchases professional association and 
governmental databases to verify an HCP's practicing status. These 
verification resources include the Drug Enforcement Agency number 
(DEA#) and the American Medical Association Medical Education Number 
(ME#).
    (Comment 13) One comment suggested that the samples should be 
prepared for heterogeneity of preference.
    (Response 13) We agree that our modeling approach is to account for 
potential preference heterogeneity. At the design phase, we are 
intentionally setting up the study to allow us to compare preference 
weights between diagnosed consumers and physicians within each health 
condition. Additionally, we intend to analyze the data using several 
modeling approaches with other sources of preference heterogeneity in 
mind.
    (Comment 14) One comment suggested the study collect respondents' 
demographic information, including race/ethnicity, income, geographical 
region, educational attainment, and healthcare system experiences, 
particularly negative experiences with an HCP due to their race; two 
comments suggested the study collect additional data on participants' 
baseline HbA1c status.
    (Response 14) We will measure several demographic variables about 
respondents, including race/ethnicity, educational attainment, gender, 
age, geographical location, health literacy, and numeracy. We will also 
collect information about time since diagnosis, perceived severity of 
their health condition, and experience/familiarity with prescription 
drugs to treat the condition. Based on prior experience, we expect 
these variables to have a direct or indirect effect on our measures. 
See also Response 13 regarding preference heterogeneity (i.e., the 
extent to which tastes and preferences vary across participants and/or 
groups). We are avoiding requesting potentially sensitive personal 
information from respondents. Although we agree that information about 
consumers' A1C status could be useful for explaining preference 
heterogeneity that we may observe, collecting data at that level of 
personal detail is not warranted given the goals of the research. 
Instead, we have included a less intrusive perceived severity measure.
    (Comment 15) One comment requested clarification of the rationale 
for determining the study's sample size (800 consumers and 800 
physicians). Another comment questioned whether the sample size per 
demographic may be insufficient to understand how these conditions 
affect different populations.
    (Response 15) The proposed sample size in the two main studies is n 
= 400 participants for each subgroup of interest (diagnosed consumers 
and physicians), for a total combined N = 1600. For our power 
estimates, we assumed an experimental design with no less than 5 
conjoint questions per participant (t = 5), 2 alternatives per question 
(a = 2), and 4 levels per attribute (c = 4). This implies a sample of 
400 participants per subgroup per study.
    (Comment 16) One comment asked that a Spanish-language version of 
the survey be included to ensure that the experiences of this 
population are included.
    (Response 16) We are limiting the survey to the English language, 
as the majority of advertising for these products is disseminated in 
English at this time.
    (Comment 17) One comment encouraged FDA to broadly and 
systematically disseminate all final results of completed research 
related to this topic.
    (Response 17) The Agency anticipates disseminating the results of 
the study after the final analyses of the data are completed, reviewed, 
and cleared. The exact timing and nature of any such dissemination has 
not been determined but may include presentations at trade and academic 
conferences, publications, articles, and posting on FDA's website.
    (Comment 18) One comment asserted that access to the choice tasks 
and proposed questions, including content-specific language and terms, 
would allow a more substantive review of the proposed research.
    (Response 18) Our questionnaires were made available during the 
public comment process. Our full stimuli are under development during 
the PRA process. We do not make draft stimuli public during this time 
because of concerns that this may contaminate our participant pool and 
compromise our research. In our research proposals, we describe the 
purpose of the study, the design, the population of interest, and the 
estimated burden.
    (Comment 19) One comment suggested considering adding a ``don't 
know'' response option throughout the questionnaire, where appropriate.
    (Response 19) We understand the value of providing such responses 
for items of a factual nature. The drawback to providing such response 
options to these questions, however, is that we may lose information by 
allowing respondents to choose an easy response instead of giving the 
item some thought. Research has demonstrated that providing ``no 
opinion'' options likely results in the loss of data without any 
corresponding increase in the quality of the data. Thus, we prefer not 
to add these options to the survey.
    (Comment 20) One comment suggested revising S5 to read ``are you 
currently being treated for the following conditions . . .''
    (Response 20) The current wording of S5 is consistent with the 
eligibility criterion that consumers self-identify as having been 
diagnosed with plaque psoriasis or type 2 diabetes. We will maintain 
this wording.
    (Comment 21) One comment noted that it is unclear what method will 
be used to achieve the literacy goal of screening question 11.
    (Response 21) The programming note for question S11 indicates that 
participants would count toward the low health literacy quota if the 
numeral value assigned to their response is greater than or equal to 3, 
where 3 = ``Sometimes,'' 4 = ``Often,'' and 5 = ``Always.''
    (Comment 22) Two comments expressed confusion about whether 
question A2 is measuring severity from the patient's or physician's 
perspective

[[Page 26557]]

and recommended clarifying the question or replacing it.
    (Response 22) We have revised question A2, as suggested, to clarify 
that we are asking about the perceived severity of the condition from 
the participant's perspective.
    (Comment 23) One comment recommended rephrasing question A6 to 
specify ``forms'' rather than ``types'' and to clarify the difference 
between a prefilled pen and a syringe (diabetes questionnaire).
    (Response 23) We have reworded question A6, replacing the term 
``types'' with ``forms.'' In the one-on-one interviews, none of the 
participants expressed confusion about the two terms.
    (Comment 24) One comment recommended revising the patient profile 
in the physician survey to reflect that most patients are diagnosed 
with type 2 diabetes in their 50s or 60s.
    (Response 24) We appreciate your recommendations concerning the 
realism of the patient profile. In consultation with a medical advisor, 
we have maintained the patient profile age of 57 years but have changed 
the diabetes duration in the patient profile from 14 years to 4 years 
to reflect more standard disease state information.
    (Comment 25) One comment suggested adding context to the diabetes 
questionnaire instructions to reduce ambiguity and facilitate 
comparisons between the physician and consumer surveys. Specifically, 
the comment suggests adding more information to the consumer survey 
about the baseline and changed A1C levels in the introduction (Section 
B).
    (Response 25) Section B introduces each attribute that will be 
varied in the DCE. The language in the Section B introduction in the 
physician and consumer questionnaires is tailored to the audience but 
has the same information about the A1C goal and point reductions that 
will be examined in the study, which will facilitate comparisons 
between the two samples. Section C provides the patient profile that 
will be used as the basis for the DCE. For physicians, the profile is 
for a hypothetical patient. For consumers, the instructions ask the 
participant to imagine their doctor recommends they try a prescription 
drug to help lower their A1C. The change in A1C levels used in the 
choice tasks for both consumers and physicians includes examples that 
are anchored to an A1C of 8.5.
    (Comment 26) One comment suggested adding itch (pruritis) as an 
attribute.
    (Response 26) In choosing and defining product attributes to 
include in the study, we selected characteristics based on evidence 
that they will impact choice. Itch relief didn't feature prominently in 
the results of our literature review or in the one-on-one interviews 
with consumers or physicians. In comparison, effectiveness at achieving 
skin clearance was an attribute in every DCE study included in our 
literature review, had the greatest relative importance in many of 
those studies, and was mentioned as an important consideration in open-
ended comments and ranked among the three most important 
characteristics by most participants in our one-on-one interviews.
    (Comment 27) One comment recommended adding more description, using 
both simple text and simple graphics, to the ``serious side effects'' 
to depict the chance of experiencing a serious side effect, and it 
recommended adding definitions for the additional attributes.
    (Response 27) Rare but serious adverse reactions/side effects will 
be presented to participants as a single attribute but may be treated 
as a set of dichotomous attributes for study design and analysis 
purposes (e.g., each side effect will be either present or absent in a 
profile). Varying more than one factor at a time within an attribute 
makes it difficult to distinguish the effect of each factor separately.
    The ``additional information'' attributes are essentially marketing 
claims; however, we have labeled the attribute ``additional information 
about the drug'' to avoid eliciting reactance from participants in 
response to the term ``marketing.'' Marketing claims are not typically 
presented with definitions, so we do not provide definitions for the 
levels of this attribute.
    (Comment 28) One comment suggested replacing ``adherence'' with 
``usage'' in the consumer questionnaires and standardizing preference 
description across the patient and physician questionnaires.
    (Response 28) We will assess participant comprehension of the term 
``adherence'' during cognitive interviews, and we can make changes, if 
indicated.
    Descriptions of the preference attribute are the same in the 
physician and consumer questionnaires within each health condition. The 
attributes for each health condition are designed to be relevant to 
that particular health condition. We do not intend to make formal 
comparisons between health conditions.
    (Comment 29) One comment suggested revising questions B1 to B5 from 
``how important is it'' to instead obtain information about prior 
experience with each attribute.
    (Response 29) The purpose of questions B1 to B5 is to collect self-
report ratings of how important each attribute is to participants, 
which we may use to validate the relative importance scores derived 
from the DCE. We derived these questions from similar questions 
included in Janssen et al. (Ref. 17), a study that was conducted to 
illustrate how DCE could be conducted when following International 
Society for Pharmacoeconomics and Outcomes Research (ISPOR) 
recommendations for good research practices.
    (Comment 30) One comment asserted that most current diabetes drugs 
are not associated with heart disease and suggested removing that 
attribute and adding questions related to weight loss and potential 
cardiovascular benefits.
    (Response 30) We agree that cardiovascular mortality is not an 
adverse reaction associated with most diabetes drugs; however, there is 
evidence of increased risk of cardiovascular mortality for some oral 
antidiabetic agents (e.g., sulfonylureas, thiazolidinediones, and 
dipeptidyl peptidase 4 inhibitors (Refs. 18 and 19); we are not 
examining use of insulin in this study). Our approach with the serious 
adverse reactions/side effects attribute is to present a range of 
category-appropriate adverse reactions that differ greatly in terms of 
severity. The reasoning is similar to that behind manipulating extremes 
in an experimental study in order to increase variance, even if the 
resulting attributes do not reflect what is typical for the category.
    (Comment 31) One comment asserted that the planned data analysis 
and how data between consumers and physicians would be compared is 
unclear.
    (Response 31) We will use a variety of statistical techniques to 
analyze the data, adapting our modeling approach to the specific 
research questions and observed characteristics of the data. A variety 
of modeling approaches can be used to estimate preference weights in 
choice-based conjoint studies (Ref. 14)--including conditional logit, 
mixed logit, Bayesian latent utility, and latent class conditional 
logistic regression models. The results of the statistical analysis 
will be used to: (1) identify which attributes of prescription drug 
products diagnosed consumers and physicians value most, (2) calculate 
the relative importance of attributes, (3) identify differences in 
preferences between the

[[Page 26558]]

two subgroups (e.g., by including interaction terms in the model), and 
(4) determine how participants make tradeoffs among attributes to make 
treatment choices. We intend to examine responses within medical 
conditions. Where commonalities in survey questions exist, we may 
compare the consumer and physician responses. Details of our research 
questions are included as part of the information collection submission 
to OMB.
    (Comment 32) One comment suggested that physicians review the 
patient survey during pretesting to ensure that the physician and 
patient surveys are aligned.
    (Response 32) Although some wording may differ between the 
physician and consumer questionnaires to reflect the knowledge and 
expertise of each sample, we have endeavored to ensure that the 
concepts are equally represented in the questionnaires across samples. 
Additionally, we have solicited peer review feedback on the 
questionnaires from experts in the field. We will also conduct 
cognitive interviews and pretests to help identify areas where the 
materials are ambiguous or confusing for participants and make any 
necessary refinements.
    (Comment 33) Three comments had questions about the purpose of the 
pretesting and the accuracy of the burden estimation for the 
pretesting, and one comment stated that the burden estimate seemed 
reasonable.
    (Response 33) We will conduct both cognitive interviews and 
pretests. The burden chart reflects both the cognitive interviews and 
the pretesting. Qualitative, one-on-one cognitive testing will be used 
to help identify areas where the materials would benefit from 
refinements. Additionally, up to two rounds of quantitative pretesting 
per study will be employed to evaluate the procedures and measures used 
in the main study. We will balance various factors that affect study 
completion time and limit the questionnaire to a mean of 20 minutes or 
less.
    The way attribute levels are combined to form hypothetical choice 
options in a choice-based conjoint analysis, or DCE, are determined by 
the study's experimental design. Although the number of possible 
combinations is often too large for each participant to evaluate them 
all, we will generate a statistically efficient design that reduces the 
number of choice tasks participants must complete while maintaining 
sufficient balance and orthogonality for reliable parameter estimation.
    (Comment 34) One comment referred to an abstract describing a DCE 
examining patients' preferences for newer second-line antihyperglycemic 
agents.
    (Response 34) We appreciate bringing the abstract to our attention.
    FDA estimates the burden of this collection of information as 
follows:

                                                       Table 1--Estimated Annual Reporting Burden
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                  Number of
                   Activity                       Number of     responses per   Total annual        Average burden per response \1\         Total hours
                                                 respondents     respondent       responses
--------------------------------------------------------------------------------------------------------------------------------------------------------
Cognitive Interview Screener, Consumers......             150               1             150  0.08 (5 min).............................              12
Cognitive Interviews, Consumers..............               9               1               9  1........................................               9
Pretest 1 Screener, Physicians \2\...........              95               1              95  0.08 (5 min).............................               8
Pretest 1 Screener, Consumers \3\............              95               1              95  0.08 (5 min).............................               8
Physician Pretest 1..........................              66               1              66  0.33 (20 min)............................              22
Consumer Pretest 1...........................              66               1              66  0.33 (20 min)............................              22
Pretest 2 Screener, Physicians \2\ \3\.......              95               1              95  0.08 (5 min).............................               8
Pretest 2 Screener, Consumers \2\ \3\........              95               1              95  0.08 (5 min).............................               8
Physician Pretest 2 \2\......................              66               1              66  0.33 (20 min)............................              22
Consumer Pretest 2 \2\.......................              66               1              66  0.33 (20 min)............................              22
Physician Main Study Screener \2\............           1,258               1           1,258  0.08 (5 min).............................             101
Physician Main Study.........................             880               1             880  0.33 (20 min)............................             290
Consumer Main Study Screener \2\.............           1,258               1           1,258  0.08 (5 min).............................             101
Consumer Main Study..........................             880               1             880  0.33 (20 min)............................             290
                                              ----------------------------------------------------------------------------------------------------------
    Total....................................  ..............  ..............           5,079  .........................................             923
--------------------------------------------------------------------------------------------------------------------------------------------------------
\1\ Burden estimates of less than 1 hour are expressed as a fraction of an hour in decimal format.
\2\ Number of screener respondents assumes a 70 percent eligibility rate with targeted recruitment.
\3\ Pretest 2 will be conducted only if changes to study materials are made in response to the findings of Pretest 1.

    As with most online and mail surveys, it is always possible that 
some participants will be in the process of completing the survey when 
the target number is reached and that those surveys will be completed 
and received before the survey is closed out. To account for this, we 
have estimated approximately 10 percent overage for both samples in the 
pretest and main study.

II. References

    The following references marked with an asterisk (*) are on display 
at the Dockets Management Staff (HFA-305), Food and Drug 
Administration, 5630 Fishers Lane, Rm. 1061, Rockville, MD 20852) and 
are available for viewing by interested persons between 9 a.m. and 4 
p.m., Monday through Friday; they also are available electronically at 
https://www.regulations.gov. References without asterisks are not on 
public display at https://www.regulations.gov because they have 
copyright restriction. Some may be available at the website address, if 
listed. References without asterisks are available for viewing only at 
the Dockets Management Staff. FDA has verified the website addresses, 
as of the date this document publishes in the Federal Register, but 
websites are subject to change over time.

1. Aikin, K.J., K.R. Betts, K.S. Ziemer, et al. (2019). ``Consumer 
Tradeoff of Advertising Claim Versus Efficacy Information in Direct-
to-Consumer Prescription Drug Ads.'' Research in Social and 
Administrative Pharmacy, 15(12), 1484-1488. https://doi.org/10.1016/j.sapharm.2019.01.012.
* 2. Arroyo, R., A.P. Sempere, E. Ruiz-Beato, et al. (2017). 
``Conjoint Analysis to Understand Preferences of Patients With 
Multiple Sclerosis for Disease-Modifying Therapy Attributes in 
Spain: A Cross-Sectional Observational Study.'' BMJ Open, 7(3), 
e014433. https://doi.org/10.1136/bmjopen-2016-014433.
3. Fraenkel, L., L. Suter, C.E. Cunningham, et al. (2014). 
``Understanding Preferences for Disease-Modifying Drugs in 
Osteoarthritis.'' Arthritis Care & Research, 66(8), 1186-1192. 
https://

[[Page 26559]]

pubmed.ncbi.nlm.nih.gov/24470354.
4. Katz, D.A., C. Hamlin, M.W. Vander Weg, et al. (2020). 
``Veterans' Preferences for Tobacco Treatment in Primary Care: A 
Discrete Choice Experiment.'' Patient Education and Counseling, 
103(3), 652-660. https://doi.org/10.1016/j.pec.2019.10.002.
5. Wouters, H., G.A. Maatman, L. Van Dijk, et al. (2013). ``Trade-
Off Preferences Regarding Adjuvant Endocrine Therapy Among Women 
With Estrogen Receptor-Positive Breast Cancer.'' Annals of Oncology, 
24(9), 2324-2329. https://doi.org/10.1093/annonc/mdt195.
6. Gregorian, R.S., A. Gasik, W.J. Kwong, et al. (2010). 
``Importance of Side Effects in Opioid Treatment: A Trade-Off 
Analysis With Patients and Physicians.'' The Journal of Pain, 
11(11), 1095-1108. https://doi.org/10.1016/j.jpain.2010.02.007.
7. Johnson, FR, E. Lancsar, D. Marshall, et al. (2013). 
``Constructing Experimental Designs for Discrete-Choice Experiments: 
Report of the ISPOR Conjoint Analysis Experimental Design Good 
Research Practices Task Force.'' Value in Health, 16(1), 3-13. 
https://doi.org/10.1016/j.jval.2012.08.2223.
8. Bridges, J.F.P., A.B. Hauber, D. Marshall, et al. (2011). 
``Conjoint Analysis Applications in Health--A Checklist: A Report of 
the ISPOR Good Research Practices for Conjoint Analysis Task 
Force.'' Value in Health, 14(4), 403-413. https://doi.org/10.1016/j.jval.2010.11.013.
9. Orme, B. (2019). Getting Started With Conjoint Analysis: 
Strategies for Product Design and Pricing Research (Fourth ed.). 
Madison, WI: Research Publishers LLC.
10. Johnson, FR, B. Kanninen, M. Bingham, et al. (2006). 
``Experimental Design for Stated-Choice Studies.'' In: Valuing 
Environmental Amenities Using Stated Choice Studies (pp. 159-202). 
B.J. Kanninen (Ed.). Dordrecht: Springer.
11. Campbell, D. and S. Erdem (2019). ``Including Opt-Out Options in 
Discrete Choice Experiments: Issues to Consider,'' The Patient--
Patient-Centered Outcomes Research, 12, 1-14. https://doi.org/10.1007/s40271-018-0324-6.
12. Feldman, S.R., S.A. Regnier, A. Chirilov, et al. (2019). 
``Patient-Reported Outcomes Are Important Elements of Psoriasis 
Treatment Decision Making: A Discrete Choice Experiment Survey of 
Dermatologists in the United States.'' Journal of the American 
Academy of Dermatology, 80, 1650-1657. https://doi.org/10.1016/j.jaad.2019.01.039.
13. Hauber, A.B., J.M. Gonz[aacute]lez, B. Schenkel,et al. (2011). 
``The Value to Patients of Reducing Lesion Severity in Plaque 
Psoriasis.'' Journal of Dermatological Treatment, 22, 266-275. 
https://doi.org/10.3109/09546634.2011.588193.
14. Hauber, A.B., J.M. Gonz[aacute]lez, C.G.M. Groothuis-Oudshoom, 
et al. (2016). ``Statistical Methods for the Analysis of Discrete 
Choice Experiments: A Report of the ISPOR Conjoint Analysis Good 
Research Practices Task Force.'' Value in Health, 19, 300-315. 
https://doi.org/10.1016/j.jval.2016.04.004.
15. Seston, E.M., D.M. Ashcroft, and C.E.M. Griffiths (2007). 
``Balancing the Benefits and Risks of Drug Treatment.'' Archives of 
Dermatology, 143, 1175-1179. https://doi.org/10.1001/archderm.143.9.1175.
16. Yang J., FR Johnson, V. Kilambi, et al. (2015). ``Sample Size 
and Utility-Difference Precision in Discrete-Choice Experiments: A 
Meta-Simulation Approach.'' Journal of Choice Modeling, 16, 50-57.
17. Janssen, E.M., A.B. Hauber, and J.F. Bridges (2018). 
``Conducting a Discrete-Choice Experiment Study Following 
Recommendations for Good Research Practices: An Application for 
Eliciting Patient Preferences for Diabetes Treatments.'' Value in 
Health, 21(1), 59-68.
18. Cavaiola, T.S. and J. Pettus (2017). ``Management of Type 2 
Diabetes: Selecting Amongst Available Pharmacological Agents.'' In: 
Endotext [internet]. K.R. Feingold, B. Anawalt, A. Boyce, et al. 
(Eds.). South Dartmouth, MA: MDText.com, Inc. https://www.ncbi.nlm.nih.gov/books/NBK425702.
* 19. Sanofi (2018). Amaryl (sulfonylurea): Full prescribing 
information, https://products.sanofi.us/amaryl/amaryl.pdf.

    Dated: April 26, 2023.
Lauren K. Roth,
Associate Commissioner for Policy.
[FR Doc. 2023-09183 Filed 4-28-23; 8:45 am]
BILLING CODE 4164-01-P


