Advancing and applying statedpreference methods among
patients with type 2 diabetes
John FP Bridges
Associate Professor
Department of Health Policy and Management
Johns Hopkins Bloomberg School of Public Health
1
Acknowledgements
•  This work is supported by the Patient-Centered
Outcomes Research Institute (PCORI) Methods
Program Award (ME-1303-5946).
•  John Bridges is supported by the FDA-Johns Hopkins
Center for Excellence in Regulatory Science and
Innovation (CERSI)
•  Co-investigators:
•  Albert Wu, Daniel Longo, Lee Bone, Karen
Bandeen-Roche, Jodi Segal, Tanjala Purnell,
Karen Edwards, Ellen Janssen, Allison Oakes,
Mo Zhou
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Overview
This presentation will
1.  Give a brief overview of stated-preference methods
and instrument development of two choice
experiments to measure the preferences of people with
type 2 diabetes
2.  Discuss results of a national survey comparing the use
of discrete-choice experiment and best-worst scaling to
measure preferences for diabetes medications
3.  Discuss preference heterogeneity for diabetes
medications using different analytical techniques
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Objectives of the PCORI study
1.  Demonstrate and disseminate good practices for
patient and community involvement in patient
centered outcomes research projects by applying
principles of community-based participatory research
2.  Address several key methodological questions
pertaining to the use of stated-preference methods
3.  Demonstrate and disseminate good practices for the
application of stated-preference methods in patient
centered outcomes research
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Aims of the PCORI study
1.  Compare two survey methods for assessing the
priorities of patients with type 2 diabetes (rating/
ranking vs. best-worst scaling)
2.  Compare two survey methods for measuring the
preferences of patients with type 2 diabetes
(choice based conjoint/discrete choice experiment
vs. best-worst scaling)
3.  Compare stratification and segmentation methods
for analyzing preference heterogeneity
4.  Assess patients’ and stakeholders beliefs about
the relevance of our methods and results
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Diabetes action board (DAB)
The diabetes action board (DAB) is a group of local and
national stakeholders that has played and will continue to
play a role in:
•  Developing this study to measure the preference
of patients in type 2 diabetes
•  Assisting in the broad dissemination of the
research findings and in leverage further
applications and action in type 2 diabetes
•  Building personal and professional relationships to
enrich our work
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PCORI
Study
StudyDiabetes
Overview -Preferences
Progress
Progress
FirstDAB
mee2ng
SecondDAB
mee2ng
Whitepaper
Report:focus
groups
ThirdDAB
mee2ng
FourthDAB
mee2ng
Report:aggregate
findings
FiMhDAB
mee2ng
Report:
heterogeneity
Systema2c
Pretest Na2onal
Review
(n=25) (n=1000)
Focusgroups
Pilottest
(n=25)
(n=50)
FinalDAB
mee2ng
Report:followup
findings
Followupsurvey
(n=600)
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
2014
2015
2016
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1. Instrument development
8
Stated-preference methods
•  Stated-preference methods are survey techniques
aimed at documenting the priorities and preferences of
respondents
•  Preferences relate to an a priori assessment of
possible alternatives (e.g. health states, treatment
options etc). Implicit in the assessment of
preferences is a tradeoff between the perceived
benefits and harms
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Preferences in Decision Making
•  Regulatory benefit-risk assessments
–  Patient Focused Drug Development
–  Medical Device Innovation Consortium
–  FDA recently approved a weight loss device based
on patients’ risk tolerance
–  FDA released Patient Preference Information draft
guidance
10
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Preferences in Diabetes
•  Chronic condition that requires lifestyle intervention
and has a range of available treatment options
•  Three systematic reviews explored the literature on
treatment preferences in diabetes (Joy et al. 2013,
Purnell et al. 2014, VonArx et al., 2014)
•  67 unique title reviewed
•  Most studies were industry funded, had small
sample sizes, and were of low to moderate
quality
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Instrument development
Evidence
synthesis
Expert
consultation
Stakeholder
engagement
Pretest
interviews
Pilot testing
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Evidence synthesis
•  Objective: To gather existing evidence on preferences
and utilize the existing literature to develop the
instrument
•  Combined articles from three literature reviews on
treatment preferences of adults with diabetes
•  Extracted attributes from discrete choice experiments
(DCE) and conjoint analyses (CA)
•  Within each study, relative attribute importance (RAI) for
each attribute was calculated
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Preferences in diabetes literature
•  10aUributeswithrela2veaUributeimportanceextracted
from12publishedDCEsondiabetespreferences
A0ributes
CVDrisk(6)
Glucosemonitoring(3)
TreatmentBurden(23)
Nausea(12)
Hypoglycemia(17)
Sideeffects(10)
Weight(14)
Glucosemeasures(19)
QualityofLife(3)
Cost(11)
0
1
2
3
4
5
6
7
8
9
StandardizedRela-veA0ributeImportance
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Max
Median
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10
Min
14
Expert consultation
•  Objective: To ensure clinical accuracy and relevance of
attributes and decision framework
•  Decision Framework:
“Suppose that your doctor says that your current
diabetes medicine is not working to keep your blood
sugar controlled. Your doctor recommends that you
add another diabetes medicine to lower your A1c.”
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Pretest interviews (n=25)
•  Objective: To ensure acceptability of the instrument to
patients and to update the instrument based on
participants’ feedback.
•  In-person semi-structured cognitive interviews (20-60
min) among patients with type 2 diabetes in Baltimore.
•  Participants were presented with a paper version of the
instrument and verbalized their thoughts.
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Pilot Testing (n=50)
•  Objective: To quantitatively test the instrument and to
estimate attribute priors for the experimental design of a
large-scale national survey.
•  Administered through a national online panel (GfK
KnowledgeNetwork)
•  Collected demographic information
•  Randomized design was utilized:
–  DCE (n=27) v BWS (n=23) to measure preferences
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Final survey - Attributes
Attributes
A1c levels go
down Stable blood
glucose Low blood
glucose
Nausea Additional
medicine Highest
benefit/
Lowest risk
Medium
benefit and
risk
Lowest
benefit/
Highest risk
1%
0.5%
0%
6 days per
week
4 days per 2 days per
week
week
During the day During the day
None
only
and/or night
30 minutes
90 minutes
None
per day
per day
1 pill and 1
1 pill per day 2 pills per day injection per
day
Additional out$10 per month $30 per month $50 per month
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2. Comparing two preferenceelicitation methods
19
DCE
•  A repeated discretechoice response
indicating preference
between two or more
profiles according to
objective/subjective
criteria
•  Strengths: most
frequently used and
studied statedpreference method
•  Limitations:
complicated design and
analysis
Attributes
A1c levels go
down
Stable blood
sugar
Medicine Medicine
A
B
1%
0.5%
2 days/wk 4 days/wk
Low blood
glucose
During the
day
None
Nausea
None
90 min/
day
Additional
2 pills/day 1 pill/day
medicine
Additional out$50/mo
$30/mo
of-pocket costs
Which medicine Medicine Medicine
A
B
would you
choose?
☐
☐
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BWS
A repeated discretechoice response
assessing the best/worst
aspect of a profile
according to objective/
subjective criteria
Strengths: simple design
and analysis
Limitations: possible
floor and ceiling effects
Attributes
Medicine
Best Worst
A
A1c levels go
1%
down
Stable blood
4 days/wk
sugar
☐
☐
☐
☐
Low blood
glucose
During the
day
☐
☐
Nausea
None
☐
☐
☐
☐
☐
☐
Additional
2 pills/day
medicine
Additional
out-of-pocket $50/mo
costs
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Experimental design
DCE
•  6 attributes at 3 levels each
•  Bayesian efficient design:
•  48 profile pairs divided into 4 blocks
•  Added 2 holdout tasks to each block
•  18 profile pairs per participant
•  Prompt: Consider the following two diabetes medicines.
Which medicine would you prefer?
BWS
•  6 attributes at 3 levels each
•  Orthogonal design:
•  18 profiles per participant
•  Prompt: Which of this medicine’s characteristics is the
best and which is the worst?
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National Survey - Overview
•  1103 participants with self-reported type 2 diabetes.
•  Survey was administered through GfK Knowledge Panel,
a nationally representative online panel.
•  The survey was in the field for 16 days from October 10
to October 25, 2015
•  Collected preference data as well as self-reported
demographic, personality, and clinical information
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Research Question
•  Are treatment preferences estimated using BWS Case 2
the same as treatment preferences estimated using
DCE?
•  Determine:
•  Respondent burden of the methods
•  Correlation between methods
•  Equivalence of the methods
•  Do different subgroups display different preferences?
•  Stratification
•  Segmentation
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Demographic characteristics
Total (N, prop)
Age (mean, range)
Gender
Male (N, prop)
Race
White (N, prop)
Black (N, prop)
Hispanic (N, prop)
Other (N, prop)
Education
No HS degree (N, prop)
HS degree (N, prop)
Some college (N, prop)
Bachelor’s or higher (N, prop)
BWS 2
DCE
551 (0.50) 552 (0.50)
63 (25, 89) 61 (24, 91)
P-value
274 (0.49) 279 (0.51)
.787
.985
.082
286 (0.51) 289 (0.52)
128 (0.23) 126 (0.23)
117 (0.21) 119 (0.22)
20 (0.04) 18 (0.03)
.393
39 (0.07) 43 (0.08)
174 (0.32) 188 (0.34)
182 (0.33) 156 (0.28)
156 (0.28) 165 (0.30)
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25
Diabetes Related Characteristics
BWS 2
Years of diagnosis (mean, range)
Hypoglycemia
At least once in past 6 mo (N,
prop)
A1c level
≥ 8.0% (N, prop)
≥ 7.0%, but < 8.0% (N, prop)
< 7.0% (N, prop)
Don’t know (N, prop)
Diabetes medicine
No medicine (N, prop)
Only pills (N, prop)
Only insulin/injection (N, prop)
Pills and injections (N, prop)
DCE
P-value
13.2
12.6
(11.9, 14.5) (11.4, 13.7)
.645
273 (0.50) 259 (0.47)
.820
.169
83 (0.15) 80 (0.15)
144 (0.27) 153 (0.28)
232 (0.43) 228 (0.41)
84 (0.15) 89 (0.16)
.049
62 (0.11) 37 (0.07)
321 (0.58) 345 (0.63)
48 (0.09) 42 (0.08)
119 (0.22) 127 (0.23)
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25 50 75 100 125 150 175 200
Time spend per Section (minutes)
Q1
Min
Median
Max
0
Q3
DCE
Task
DCE
BWS 2
BWS2
N
Median
Min
Q1
Q3
Max
(minutes) (minutes) (minutes) (minutes) (minutes)
552
10.1
0.9
8.8
16.6
191.6
551
12
1.3
7.4
14.8
146.7
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Evaluation of preference tasks
Standardizedscoreonascalefromstronglydisagree(-2)tostrongly
agree(+2)
1.2000
1.0000
0.8000
0.6000
DCE
BWS2
0.4000
0.2000
0.0000
Ifounditeasyto
understandthe
ques2ons
Ifounditeasyto
completetheques2ons
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Iansweredinaway
consistentwithmy
preferences
28
DCE vs. BWS Case 2 (rho = 0.93)
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$50
$30
Out-ofpocketcost
$10
2pills
1pill
90minutes
30minutes
BWS
Treatment
burden
1pilland1injec2on
DCE
Nausea
None
Day
None
Lowblood
glucose
Dayand/ornight
Analyzedusingmixedlogit
andeffectscoding
2days/week
4days/week
Stableblood
glucose
6days/week
0%
0.50%
1%
1.6
A1c
1.4
1.2 decrease
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
-1.4
-1.6
29
DCE vs. BWS Case 2 - scaled
0.7
A1c
decrease
Stableblood
glucose
Lowblood
glucose
Nausea
Treatment
burden
Out-ofpocketcost
0.5
0.3
0.1
-0.1
-0.3
-0.5
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$50
$30
$10
2pills
1pill
90minutes
30minutes
BWS
1pilland1injec2on
DCE
None
Day
None
Dayand/ornight
Analyzedusingmixedlogit
andeffectscoding
2days/week
4days/week
6days/week
0%
0.50%
-0.9
1%
-0.7
30
Standardized attribute importance
12
10
8
6
DCE
4
BWS
2
0
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Comparing results with the literature
A0ributes
CVDrisk(6)
Glucosemonitoring(3)
TreatmentBurden(23)
Nausea(12)
Hypoglycemia(17)
Sideeffects(10)
Weight(14)
Glucosemeasures(19)
QualityofLife(3)
Cost(11)
0
1
2
3
4
5
6
7
8
9
10
StandardizedRela-veA0ributeImportance
BWS
DCE
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Max Median
Min
32
3. Preference heterogeneity
33
Preference heterogeneity
•  Different individuals or individuals of different
subgroups might have different preferences.
•  Preference heterogeneity can be accounted for using:
•  Stratification
•  Mixed logit models
•  Finite mixture models (segmentation)
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Preferences by Gender
0.7
A1c
decrease
Stableblood
glucose
Nausea
Lowblood
glucose
Treatment
burden
Out-ofpocketcost
0.5
0.3
0.1
-0.1
-0.3
-0.5
Female
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$30
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2 pills
1 pill
90 minutes
1 pill and 1 injection
Male
30 minutes
None
Day
None
Day and/or night
Analyzedusingcondi4onal
logitandeffectscoding
2 days/week
4 days/week
6 days/week
0%
0.50%
1%
-0.7
35
Preferences by A1c levels
1.2
1.0
A1c
decrease
Stableblood
glucose
Lowblood
glucose
Nausea
Treatment
burden
Out-ofpocketcost
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
>8%
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$30
$10
2 pills
1 pill
90 minutes
1 pill and 1 injection
<7%
30 minutes
None
Day
None
Day and/or night
Analyzedusingcondi4onal
logitandeffectscoding
2 days/week
4 days/week
6 days/week
0%
0.50%
1%
-1.4
36
DCE – Mixed Logit
2.1
1.3
0.9
0.5
0.1
-0.3
-0.7
-1.1
-1.5
-1.9
A1c
decrease
Stable blood
glucose
Low blood
glucose
Nausea
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Treatment
burden
$50
$30
$10
1 pill and 1 injection
2 pills
1 pill
90 minutes
30 minutes
None
Day and/or night
Day
None
2 days/week
4 days/week
6 days/week
0%
0.50%
-2.3
1%
Mean preference weights with 95% Confidence Interval
and 95% CI of preference distribution
1.7
Out-of-pocket
cost
37
Predicted individual preferences
.8
.6
.8
.6
.4
.4
.4
.2
.2
1
.2
.2
.2
1
1
.5
01
-24
0
2
-2
0
2
0
-2 2 -1
4
40
4
1.5
1.5
4
4
2
2
-1
-1
0
0
1
1
2
2
2 pills
2 pills
2
1
1
1
3
3
3
.5
.5
2
1
.5
-1
Density Density
Density
Predictedindividual
coefficients
01
.5
-.5
y
0
0
0
-11 -.5
0 -.5 .5
-.5
0
.5
1
-.51
0
y
0
.5
.5
-1
1
0
10
0
0-1 .5
-1.5 -.5 1 0
.5 -11 -.5
-.5
1
-1
-.5
0
.5
1
.5
1
1
0
1
.5
0
.5
.5
0
.5
0
0
0
-.51
0
0
.5
-.5
0
0
0
2
1
1
1
1
1
1
0
1
1
0
-.5
1.5
1.5
2
2
2
2
1.5
2
2
3
3
3
4
2
4
3
3
3
0.5% A1c decrease
4 dy/wkhypoglycemia
Daytime hypoglycemia
30 minutes Nausea
0.5% A1c decrease
Stable 4 dy/wk Stable
Daytime
30 minutes Nausea
2 pills
0.5% A1c decrease
Stable 4 dy/wk
Daytime hypoglycemia
30 minutes Nausea
4
-2
2
1
-2
1.5
-2.5
1
0
0
.2
0
.5
0
-.5
1
0
.5
-.5
0
-1 -.5 0 .5
1.5
03 .5 1 1.5
-.5 1 0
-1 -.5 0 .5 1 1.5
0
0
0
0
0
1 -.5
2
2 -23 -1 0 -1
-1 0 1 2 3
1
-2
0
0
1 pill
1
.8
.6
.4
.4
1
.5
0
.5
.2
0
.2
0
-1
1 pill
.6
.4
2
2
2
1
1
.4
.4
.4
.2
0
-2
1
1
.6
.6
3
3
1.5
1.5
.6
.6
.6
1.5
.8
.8
.8
2
3
2
2
1% A1c decrease
dy/wk
No hypoglycemia
No nausea 1 pill
1% A1c decrease
Stable 6 dy/wk Stable 6No
hypoglycemia
No nausea
1% A1c decrease
Stable 6 dy/wk
No hypoglycemia
No nausea
01
1
-1
2-1
0
0
1
1
2
2
y
Es2mateddistribu2on
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Latent Class Analysis - DCE
1.9
1.7
1.5
A1c
decrease
Stableblood
glucose
Lowblood
glucose
Nausea
Treatment
burden
Out-of-pocket
cost
1.3
1.1
0.9
0.7
0.5
0.3
0.1
-0.1
-0.3
-0.5
-0.7
-0.9
-1.1
-1.3
-1.5
-1.7
-1.9
-2.1
Class 1
Class 2
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$50
$30
$10
2 pills
1 pill and 1 injection
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1 pill
90 minutes
30 minutes
None
Day and/or night
Day
None
2 days/week
4 days/week
6 days/week
0%
0.50%
1%
-2.3
Latent Class Analysis (cont.)
Class1(65.8%)
Class2(34.2%)
Cost4%
Control12%
Control17%
Cost26%
Dose19%
Stable10%
Stable15%
Hypo10%
Dose21%
Hypo11%
Nausea10%
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Nausea45%
Latent Class Analysis (cont.)
OddsRaCo(SE)
Age
1.033**(0.01)
Female
0.979(0.21)
Race/Ethnicity
Black
1.149(0.30)
Hispanic
0.623(0.18)
Other
0.855(0.50)
Yearsofdiabetes
1.031*(0.01)
Self-reportedHealthStatus
Verygood
1.719(0.82)
Good
1.502(0.72)
Fair
1.512(0.79)
Poor
1.655(1.12)
HaveotherchroniccondiCons
1.703*(0.45)
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*Othercovariatesincludeeduca4on,income,employmentstatus,andhavingcomplica4ons.
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Conclusion
•  Participants did not express a strong preference
towards BWS or DCE.
•  Preference weights obtained from BWS or DCE had
high correlation, but were on a different scale.
•  Significant preference heterogeneity was observed in
mixed logit models and latent class models.
© 2014, Johns Hopkins University. All rights reserved.
© 2014,Johns
JohnsHopkins
HopkinsUniversity.
University.All
Allrights
rightsreserved.
reserved.
©2015,
42
Protecting Health,
Saving Lives—
Millions at a Time
43