Market Claims in DTC Print Ads SS Part B 2016

Market Claims in Direct-to-Consumer (DTC) Prescription Drug Print Ads
OMB Control No. 0910- NEW
Supporting Statement Part B

B. Statistical Methods (used for collection of information employing statistical methods)
1. Respondent Universe and Sampling Methods
The eligible study population is U.S., non-institutionalized adults age 18 and older that
have been diagnosed with diabetes (as measured by self-report). The sample will be
balanced on age, gender, race, ethnicity and region within the US to the extent possible.
The selected sample will be drawn from Ipsos’s opt-in online survey panel, i-Say. The iSay panel consists of over 800,000 members within the US. Members provide extensive
individual and household demographic information, such as gender, age, race, ethnicity,
education, income, health profile, and many other factors. Ipsos uses this information to
target recruitment of groups of interest. Here, we will recruit panelists that report having
diabetes.
Panelists identified as having been diagnosed with diabetes will be invited to participate
in the survey via email. The email indicates the given compensation for completing the
survey (i-Say points, which may be redeemed for cash or prizes) and provides a hyperlink
to the survey. Up to two reminder e-mails will be sent after the initial invitation if no
response has been received, which helps increase response rates.
Upon entering the survey, respondents are screened to confirm that they have diabetes.
Individual who work in the health care, marketing, advertising, or pharmaceutical
industries will be excluded via screening. Eligible respondents are then shown a consent
language and asked whether they agree to participate in the study. Non-eligible
respondents are thanked for their time and terminated from the survey.
We will exclude pretest study participants from the main study and follow-up study.
2. Procedures for the Collection of Information
Design Overview
The design consists of two parts; a main study and a follow-up study. We will conduct
two sequential pretest waves prior to the main study and one pretest prior to the followup study. The purpose of the pretests are to 1) ensure the stimuli are understandable and
viewable, 2) identify and address any challenges to embedding the stimuli within the
online survey, and 3) ensure the study questions are appropriate and meet the study’s
goals.

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Participants in the main study will be randomly assigned to view one of nine versions of
an ad, as depicted in Exhibit 1. The two variables of interest are type of market claim (#1
Prescribed, New) and type of efficacy information (High, Low, or none). Efficacy
information will be operationalized in the form of realistic quantitative information (for
example, “46% of patients felt their nerve pain reduced by at least half, compared to
baseline”).
Exhibit 1: Main Study Design

High
Efficacy
Level
Low
Information None (control)

Type of Market Claim
#1 Prescribed
New
None (control)
A
B
C
D
E
F
G
H
I

In the follow-up study, participants (n = 216) will complete a 15-minute paired choice
experiment. Participants will be asked to choose between two hypothetical drugs based
on print ads, one of which includes a market claim from the Main Study (#1 Prescribed or
New). The ads also include different efficacy information (for example, “46% of patients
felt their nerve pain reduced by at least half, compared to baseline” versus “51% of
patients felt their nerve pain reduced by at least half, compared to baseline”). Exhibit 2
depicts an example choice. Participants are asked to indicate which drug they would
prefer. They are given 48 such choice sets, which vary in efficacy information and the
presence of the market claim.
Exhibit 2: Example choice in the follow-up study.
Drug A

Drug B

# 1 Prescribed

46% of patients
felt …

51% of patients
felt …

Procedure
Pretests: Each participant will be randomly assigned to view a print ad for a fictitious
prescription drug indicated to treat diabetic neuropathy and will be asked to complete an
online survey assessing their benefit/risk perceptions, intentions, and attitudes toward the

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drug. Based on the pretest findings, we will revise and remove poorly performing survey
items prior to full-scale testing.
Main study: Each participant will be randomly assigned to view a print ad for a fictitious
prescription drug for diabetic neuropathy and will be asked to complete an online survey
assessing their benefit/risk perceptions, intentions, and attitudes toward the drug.
Follow-up study: Each participant will be asked to view a series of pairs of print ads for a
product that treats diabetic neuropathy. One ad will contain a market claim. Both ads will
contain quantitative efficacy information that varies along a continuum of effectiveness
in a series of 48 trials. In each comparison, participants will be asked to choose one of
the two drugs.
Participants
Eligible consumer participants for the pretests (N = 612), main study (N = 495), and
follow-up study (N = 216) will be adults who speak English and self-identify as having
been diagnosed with diabetes. We will exclude individuals who work in the health care,
marketing, advertising, or pharmaceutical industries. We will also exclude pretest study
participants from the main and follow-up studies.

Analysis Plan
Main Study: We will conduct ANOVAs (for continuous variables) and chi-squares and
logistic regressions (for categorical variables) to examine the impact of market claim and
quantitative efficacy information. Before conducting analyses, we will assess whether
the inclusion of covariates is justified. If they are, we will conduct the analyses both with
and without covariates (e.g., sex, age, race/ethnicity, education, numeracy) included in
the model. If the one-way ANOVA is significant, we will implement a series of two-way
comparisons (e.g., #1 Prescribed vs. control, #1 Prescribed + high efficacy quantitative
info vs. control, #1 Prescribed vs. #1 Prescribed + high efficacy quantitative info) to test
for significant differences among the experimental arms.
Follow-up Study: Logistic regression will be applied to the binary choices (i.e., drug
choice) made by participants on each trial. The regression will indicate the probability of
choosing one of the drugs as a function of the difference in efficacy, for each participant.
From the regression equation, we can determine the “equal point” between the two drugs;
in other words, the difference in efficacy at which the participant is equally likely to
choose either drug. The null hypothesis is that the “equal point” is when there is no
difference in efficacy. Alternatively, if market claims influence participants’ decision
making, the equal point will not be zero. For instance, if participants prefer a drug that is
associated with a claim, participants will choose the drug without a claim only if it has a
5% greater efficacy than “#1 Prescribed” drug. In this way, we can quantify the
advantage of a claim in units of efficacy.

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Power
The main study will include 495 consumer participants. We conducted a power analysis
for a 3x3 analysis of covariance (ANCOVA) using G-Power.1 The analysis assumed four
degrees of freedom in the denominator, a power level of 0.90, an α-level of 0.05 and
allowed two covariates. Using a small to medium effect size of 0.18, the required sample
size is 495 (481, adjusted upward to allow an equal number of respondents per
experimental condition).
The follow-up study will include 216 participants to obtain 90% power to observed a
small effect size (.1) at α = 0.0.5. The critical analysis is the comparison of the efficacy
difference at which the two drugs are equally likely to be chosen. An estimate is
calculated for each participant, and the estimates are compared to the null hypothesis of
zero in a one-sample 2-tailed t-test.
3. Methods to Maximize Response Rates and Deal with Non-response
This experimental study will use existing research panels to draw a sample. The consumer
panels comprise of individuals who have signed up to participate in online studies. To help
ensure that the participation rate is as high as possible, FDA will:
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Design an experimental protocol that minimizes burden (clearly written and with
appealing graphics);
Administer the survey over the Internet, allowing respondents to answer questions
at a time and location of their choosing;
Field the survey for 2 to 4 weeks to allow participants reasonable time to access
and complete the survey;
Provide up to 2 e-mail reminders throughout the course of the field period;
Provide a Member Services contact person for respondents to contact via email if
they have questions or technical difficulty as they complete the survey.

There are several approaches to address the potential for nonresponse bias analysis in this
study, such as comparing response rates by subgroups, comparing respondents and
nonrespondents on frame variables, and conducting a nonresponse follow-up study.2 For
the proposed project, we will compare responders and nonresponders on demographic
variables.
4. Test of Procedures or Methods to be Undertaken

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Faul, F. (2010). G*Power Version 3.1.3.
Office of Management and Budget, Standards and Guidelines for Statistical Surveys, September, 2006.
https://www.whitehouse.gov/sites/default/files/omb/inforeg/statpolicy/standards_stat_surveys.pdf. Retrieved March
21, 2016.

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The stimuli and draft questionnaire were tested in cognitive interviews. The cognitive
testing examined the stimuli and draft measures to refine the stimuli, improve question
wording, and narrow the pool of questions. Additionally, we will conduct pretesting to
test and further refine the measurements to be used in the main study.
5. Individuals Consulted on Statistical Aspects and Individuals Collecting and/or Analyzing
Data
The contractor, Ipsos, will collect and analyze the data on behalf of FDA as a task order
under Contract HHSF223201400503G. Aysha Keisler, Ph.D., 202-420-2021, is the Ipsos
Project Director for this project. Data analysis will be overseen by the Research Team,
Office of Prescription Drug Promotion (OPDP), Office of Medical Policy, CDER, FDA,
coordinated by Kathryn J. Aikin, Ph.D., 301-795-0569, and Kevin R. Betts, Ph.D., 240402-5090.

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