What are Patient Preferences, How Do You Measure Patient Preferences, and How Do I Use Them?

What Are Patient Preferences,
How Do You Measure Patient
Preferences, and
How Do I Use Them?
Deborah A Marshall, PhD
Professor and Arthur J.E. Child Chair in
Rheumatology Research
Cumming School of Medicine
OARSI, Toronto, May 3, 2019

2
At the end of this session participants will be able to:
1. Differentiate between patient reported outcome and
experience measures (PROMs and PREMs) and patient
preferences
2. Identify and describe good research practices to develop,
design, and execute a patient preferences survey
3. Describe how patient preferences can be measured and
how to interpret the results from patient preferences
studies
4. Identify ways in which patient preferences can be applied in
clinical practice for patients with osteoarthritis
Learning Objectives

What are Patient Reported Outcome Measures
(PROMs) and Patient Reported Experience
Measures (PREMs)?
PROMs PREMs
Umbrella term that includes
outcome data reported directly by
the patient
Encompasses range of interactions that
patients have with health care system - care
from doctors, nurses, and staff in hospitals,
health care facilities
Includes global impressions,
functional status, well-being,
symptoms, health-related quality of
life, satisfaction
ACR core set of 3 PROMs: physical
function, pain, global assessment
of disease activity
Communication with doctors and nurses; Pain
management; Timeliness of assistance;
Explanation of medications administered;
Different from patient satisfaction (which
relates to meeting expectations)
Example:
Health Assessment Questionnaire
(HAQ)
Example: Hospital Consumer Assessment of
Healthcare Providers and Systems Survey
(HCAHPS)
- Van Tuyl et al. Rheum Dis Clin N Am 2016; Felson et al. Arth Rheum 1993; Felson et al. Arth Rheum 1995 ; Manery et al. N Engl J Med 2013

Beyond PROMS and PREMS –
Preferences Consider Choices and Trade-Offs
• Experimental survey methods that ask respondents
to express the relative desirability or acceptability of
features that differ amongst alternatives …which
reflects their underlying utility for that alternative
Attributes Treatment A Treatment B
Functional Improvement 20% improvement 40% improvement
Side Effects Mild Moderate
Mode of Administration Oral Injection
Which Treatment would you
choose? □ □
- Medical Device Innovation Consortium Framework for Patient-Centered Benefit-Risk Assessment, 2015

Arthritis is a ‘preference sensitive’ condition
Treatments are preference sensitive with a key issue of
compliance and adherence to therapy.
5

Patient Preference Methods
Medical Device Innovation Consortium Catalogue of Methods
Source: MDIC PCBR Framework Report Release Event, May 13, 2015.
Available at: http://mdic.org/pcbr-framework-report-release/
Group Method
Structured-
weighting
• Simple direct weighting
• Ranking exercises
• Swing weighting
• Point allocation
• Analytic hierarchy process
• Outranking methods
Health-state
utility
• Time tradeoff
• Standard gamble
Stated-
preference
• Direct-assessment questions
• Threshold technique
• Discrete-choice experiments
• Best-worst scaling exercises
Revealed-
preference
• Patient-preference trials
• Direct questions in clinical trials

Preference Methods
• For uni-dimensional decisions
(i.e., consider one attribute or outcome at a time)
– Standard Gamble
– Time tradeoff
– Contingent valuation
• For multi-dimensional decisions
(i.e., consider multiple attributes or outcomes
simultaneously)
– ConjointAnalysis
– Best-Worst Scaling (BWS)
– Discrete-Choice Experiments (DCE)*
– Analytic Hierarchy Process (AHP)
7

– Respondents choose amongst a set of alternatives
– Each alternative is a profile defined by attributes
– (e.g., efficacy, tolerability, mode of administration, cost, etc…)
– Each attribute can take on different levels
– For example, if efficacy attribute is defined as a response rate, then
levels could be
– 60 out of 100 (60%)
– 75 out of 100 (75%)
– 85 out of 100 (85%)
Direct Preference Elicitation with Discrete-
Choice Experiment (DCE)

– Profiles are combined into sets in each choice task
– Alternative choice formats can include
• two or more active alternatives (forced choice),
• opt-out or status quo (neither or none)
• Each respondents completes a series of choice tasks
• Each choice task has a different set of profiles determined by an
experimental design
• The key to a DCE is that one alternative is chosen in each
choice task
Direct Preference Elicitation with Discrete-
Choice Experiment (DCE)

Anatomy of a DCE Choice Task
Attributes
Prefer 1 Prefer 2
X Prefer 1 Prefer 2
X
Outcome Medicine 1 Medicine 2
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
Which medicine would you
prefer?
Prefer 1 Prefer 2
Attributes
Benefit
Risk

Attribute Levels
Prefer 1 Prefer 2
X Prefer 1 Prefer 2
X
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
prefer?
Prefer 1 Prefer 2
Attribute
Levels

Profile
Prefer 1 Prefer 2
X Prefer 1 Prefer 2
X
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
prefer?
Prefer 1 Prefer 2
Profile
Attributes
Benefit
Risk

Choice
Prefer 1 Prefer 2
X Prefer 1 Prefer 2
X
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
prefer?
Prefer 1 Prefer 2
X
Choice

Series of Choice Tasks
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
prefer?
Prefer 1 Prefer 2
X
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
prefer?
Prefer 1 Prefer 2
X
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
prefer?
Prefer 1 Prefer 2
X

DCE Choice Task
Give it a Try
Car A Car B
Color Grey Red
Type 4 door sedan 2 door sport
Safety rating 5 stars 3 stars
Your choice
15

DCE Choice Task
Now what?
Car A Car B
Color Grey Red
Price $20,000 $35,000
Your choice
16

Choices Reveal Information about
Preferences
Car A Car B
Color Grey Red
Price $20,000 $35,000
Your choice X
17
 Something is only of value if we are willing to
give something up for it

Utility Estimates
Assumes the
utility associated
with an alternative
or profile is a
function of
observed
characteristics
(attributes levels)
and unobserved
characteristics of
the alternative
18
The utility of each medicine is
the sum of the effect of each level
Utility(Medicine 1) > Utility(Medicine 2)
Prefer 1
X
Prefer 1 Prefer 2
X Prefer 1 Prefer 2
X
Response Rate
90 out of 100
(90%)
50 out of 100
(50%)
Adverse Event Rate
10 out of 100
(10%)
1 out of 100
(1%)
prefer?
Prefer 1 Prefer 2
X

Using DCEs to Measure Preferences
 DCE is a robust quantitative method
grounded in economic theory to measure the
value of alternative choices and risk-benefit
trade-offs for specific attributes
 Measure how people value components
(attributes) of a product or service
 Eg. For a drug - cost, side effects, delivery mode
 Including non-health outcomes
19

20
1. Differentiate between patient reported outcome and experience
measures (PROMs and PREMs) and patient preferences
3. Describe how patient preferences can be measured and how to
interpret the results from patient preferences studies
Learning Objectives

ISPOR Task Forces on Good Research
Practices (GRPs)
https://www.ispor.org/workpaper/ConjointAnalysisGRP.asp

22
ISPOR Task Forces on Good Research
Practices (GRPs)
https://www.ispor.org/workpaper/ConjointAnalysisGRP.asp
“Aligning health care policy with patient preferences
could improve the effectiveness of health care
interventions by improving adoption of, satisfaction
with, and adherence to clinical treatments.”

10-point Checklist for Good Research
Practice in CA in Health
The checklist
generally follows
the steps required
to conduct a
conjoint analysis
study
Attributes
and levels
Define Objective
Design and
implement
survey
instrument
Design
experiment
Analyze data
Report Results
However, some of
these categories
require multiple
steps

• Was a well-defined research question stated and
is conjoint analysis an appropriate method for
answering it?
– Was a well-defined research question and/or testable hypothesis
articulated?
– Was the study perspective described and the study placed in any
particular decision-making or policy context described?
– What is the justification for using conjoint analysis to answer the
research question?
Task #1: Defining the Research Question

 Ranking the importance of attributes
 Examining tradeoffs
 Estimating willingness to pay
 Exploring variation in preferences
– Within a population
– Between two groups of stakeholders
 Evaluating potential market share
Possible Research Questions

• Were the attributes and attribute levels
supported by evidence?
– Were all important and relevant attributes identified (that is, supported
by literature reviews, focus groups, or other scientific method)?
– Was the choice of included attributes justified and consistent with
theory?
– Were the range and number of levels for each included attribute
justified?
Task #2:
Determining Attributes and Levels

Qualitative Work as a Foundation to
Stated Preferences…
- Louviere, Hensher and Swait, 2008
- Lanscar and Louviere, 2008
“We cannot overemphasise how important it is to conduct this kind of
qualitative, exploratory work to guide subsequent phases of the
stated preference study.
“It is highly recommended that qualitative work is conducted during
attribute development…the study team should endeavour to
understand the dimensions…along which the product is evaluated by
consumers and how specific levels of these dimensions are
expressed.”
“There is scope to move beyond simplistic and ad hoc uses of
qualitative tools before, alongside and after quantitative data
collection.”

Attributes: Consider all potential attributes, but in context of
plausibility
 What attributes are important to people
 Number of attributes relevant to research question
 Omitted attributes adversely affect study quality
 Understand how people discuss attributes
 What words or phrases do they use?
 Understand any interactions between the attributes
 Are attributes considered together?
 Does the preference for one attribute depend on the level of another
attribute (e.g. route of drug administration and drug regimen)
Levels: Encompass salient range of values, even if
hypothetical or not currently available
Qualitative Methods for Attribute Development

• Was the construction of the conjoint tasks
appropriate?
– Was the number of attributes in each conjoint task
justified?
– Was the number of scenarios in each conjoint task
justified?
– Was the number of conjoint tasks included in the data
collection instrument appropriate?
Task 3 Checklist : Construction of Tasks

Survey Development and Administration
30
Qualitative
Research
Pretest
Pilot Test
Data Collection
Conceptual Framework
Identify Key Attributes
Range of Levels
Cognitive Feasibility
Administrative Feasibility
Administration

How many Attributes and Levels?
31
 # attributes ranged from 3 to 16
 70% with 3 to 7 attributes
 40-50% included cost as an attribute
 # levels ranged from 2 to >6
- Marshall DA et al. The Patient 2010
- deBekker-Grob et al, Health Econ 2010

Number of Valuation Tasks Appropriate?
~ 10-20 valuation tasks in a choice set
Aim to:
– Avoid respondent fatigue
– Maximise information per respondent
– Minimise fractional design
32- Marshall DA et al. The Patient 2010
- deBekker-Grob et al, Health Econ 2010

• Was the choice of experimental design
justified and evaluated?
• Was the choice of experimental design justified?
• Were alternative experimental designs considered?
• Were the properties of the experimental design
evaluated?
• Was the number of conjoint tasks included in the
date-collection instrument appropriate?
Task #4: Experimental Design

Principle of an experiment (the researcher
controls the stimuli):
– To vary one or more attributes with two or
more levels and elicit a behavioural
response
– DCEs systematically vary attributes and
levels to investigate the determinants of
choice in a particular context.
Experimental Design

Full Factorial Design (all possible alternatives)
• Product of # Levels for each attribute (grow quickly!)
• 2 attributes with 2 levels =
• 3 attributes with 3 levels =
Factorial Design

Full Factorial Design (all possible alternatives)
• Product of # Levels for each attribute (grow quickly!)
• Estimate main effects and all interactions
Fractional Factorial Design (subset of all possible alternatives)
– Select subset randomly (potentially biased unless very large sample size) or
systematically (experimental design) to estimate effects
– May not be able to estimate all interactions
Factorial Design

Criteria to Consider Desired Criteria
Correlations among attributes Attributes are independent
Level balance Completely balanced
Number of overlapping attributes Minimal overlap
Efficiency score Higher is better, but relative
Restrictions on implausible
combinations
No implausible
combinations
Cognitive difficulty Low cognitive burden
Complexity to generate design Simple to implement
Considerations in Experimental Design

What is the Right Sample Size?
Effect on Estimate Precision
- Johnson FRJ et al. Value in Health, 2013; Yang JC al J Choice Modelling,2015
...precision
varies with the
inverse of the
square root of
sample size.

39
Task 6 Checklist: Instrument Design
• Was the data collection instrument designed
appropriately?
• Was appropriate respondent information collected
(such as sociodemographic, attitudinal, health
history or status, and treatment experience)?
• Were the attributes and levels defined, and was any
contextual information provided?
• Was the level of burden of the data-collection
instrument appropriate? Were respondents
encouraged and motivated?

An Example of a Survey Outline
Including a DCE
•Confirming inclusion and exclusion criteriaScreening
•Signed informed consent for face-to-face interviews
•Online informed consent (“I agree to participate”) for online
surveys
Informed consent
•Experience with disease
•Experience with disease treatment and managementBackground questions
•Descriptions of each attribute included in the conjoint tasks
•Warm-up questionsInformation treatment
•8-16 DCE Choice Task questions
•# tasks depends on the number of attributes and levels
•Determined by experimental design
Choice Task questions
•Age, gender, martial status, education, etc.Demographic questions
40

41
Task #7 Checklist: Data Collection
• Was the data-collection plan appropriate?
• Was the sampling strategy justified (for
example, sample size, stratification, and
recruitment)?
• Was the mode of administration justified and
appropriate (for example, face-to-face, pen-
and-paper, web-based)?
• Were ethical consideration addressed (for
example, recruitment, information and/or
consent, compensation)?

Mode of Administration
 Pen and paper survey by mail
 Pen and paper survey in-person
 Telephone assisted survey by interviewer
 Computer-based survey in person
 Television-based survey at home
 Internet survey
42

• Were statistical analyses and model
estimations appropriate?
• Were respondent characteristics examined and
tested?
• Was the quality of the responses examined (e.g.
Rationality, validity, reliability)
• Was model estimation conducted appropriately?
Task #8 Checklist: Statistical Analysis

 Data setup
 Coding attribute levels – dummy or effects coding
 Setting up the data for each choice question
 Setting up the data for each respondent
 Estimation
 Conditional logit (the foundation)
 Extensions of conditional logit
 Calculations
 Marginal rates of substitution (tradeoffs)
 Scenario Analysis 44
Analysis Steps

The Utility Function
Ui = V(β,Xi ) + εi
where
• V is the value (utility) function
• X is a vector of attribute levels
• β is a vector or parameters (preference weights)
• ε is a random error term

Conditional Logistic Regression Analysis
• Main effects
• Assumes relative preferences for each attribute level are
independent of the level of any other attribute in the
profile
• β1*attribute1 + β2*attribute2 + β3*attribute3 + ε
• Interaction effects
• Models preferences for an attribute level as dependent
on the levels of other attributes in the profile
U = …β12*attribute1*attribute2 + β13*attribute1*attribute3 + …
46

Marginal Rates of Substitution to Estimate
Risk Benefit Trade-Offs
 Indirect utility (value) function:
V = α + β1X1 + β2X2 + β3X3
 Marginal rates of substitution:
• - (βk / βj)
•(if X1 and X2 are continuous and linear)
47

49
preferences
3. Describe how patient preferences can be measured
and how to interpret the results from patient
preferences studies
Learning Objectives

Osteoarthritis Treatment
Benefits and Risks
Category Attributes Levels
Benefits
Ambulatory pain*
None (0mm)
Mild (25mm)
Moderate (50mm)
Severe (75mm)
Resting pain*
Stiffness*
Difficulty doing daily activities*
Risks
Ulcer risk**
None
10 out of 1,000 (1%)
50 out of 1,000 (5%)
100 out of 1,000 (10%)
Stroke risk***
None
5 out of 1,000 (0.5%)
15 out of 1,000 (1.5%)
30 out of 1,000 (3%)
50
Hauber et al., Osteoarthritis Cartilage, 2013
* After treatment, measured on a 100mm visual analog scale
** Incremental treated-related risk in the next year
*** Incremental treatment-related risk in the next 5 years
Benefits - Most important reductions:
- ambulatory pain and difficulty doing daily activities (both: 6.32)
- resting pain (2.80)
- stiffness (2.65)
Risks – Most Important for incremental changes (3%):
- Risk of MI (10.00)
- Stroke (8.90)

Surgeon Referral and Wait Times for
Total Joint Replacement
- Marshall DA, Deal K, Conner-Spady B, Bohm E, Hawker G, Loucks, L MacDonald KV,
Noseworthy T. How do Patients Trade-Off Surgeon Choice and Waiting Times for Total
Joint Replacement: A Discrete Choice Experiment. Osteoarthrits and Cartilege
2018;26:522-530.
- Damani Z, Spady C, Nash T, Stelfox T, Noseworthy T, Marshall DA. What is the
influence of single-entry models on access to elective surgical procedures?: A
systematic review. BMJ Open Feb 2017;7(2):e012225.
- Connor-Spady BL, Marshall DA, Hawker GA, Bohm E, Dunbar MJ, Frank C,
Noseworthy T. You’ll know when you’re ready. How do patients decide when the time
is right for joint replacement surgery? BMC Health Services 2014;14:454
- Connor-Spady BL, Marshall DA, Bohm E, Dunbar MJ, Loucks L, Hennigar A, Frank C,
Noseworthy T. Patient factors in referral choice for total joint replacement surgery.
Medical Care 2014;52(4):300-306

Arthroplasty is a
Preference-Sensitive Procedure

Next Available Surgeon: Simplified
Example
53
Attributes Levels
Surgeon
reputation
• Excellent
• Good
• Satisfactory
• Don’t know
Surgeon referral
• Selected by you
• Next available surgeon
• Selected by your doctor
Time to surgeon
consultation
• 1 month
• 6 months
• 12 months
• 18 months
Assume all other attributes of surgery (time to surgery and time
to hospital) are the same between options.
*Please note: this example does not use data from the published results (referenced in
previous slides) given the complexity of the full DCE. This is being used as a simplified
example and therefore results do not represent results reported from our published study.

Next Available Surgeon:
Simplified Example Choice Task
54
If you were told at the time of referral to a surgeon that these
were the only scenarios available, which one would you choose?
Scenario A Scenario B
Surgeon reputation Excellent Satisfactory
Surgeon referral Selected by you Selected by your doctor
Time to surgeon
consultation
18 months 6 months
I would choose X

Simplified Results
55
Variable Coefficient P value
Surgeon reputation: excellent
(reference = don’t know)
2.0 <0.001
Surgeon referral: surgeon selected by
you
(reference = selected by your doctor)
0.60 <0.001
Wait time (months) -0.20 <0.001
• Excellent reputation preferred to don’t know reputation
• Surgeon selected by you preferred to surgeon selected
by your doctor
• Shorter wait times are preferred to longer wait times

Willingness to Wait for Surgeon with
Excellent Reputation
56
• How long are patients willing to wait to have a
consultation with an excellent surgeon compared to not
knowing their surgeon reputation?
• What does this mean?
Surgeon reputation: excellent
2.0 <0.001
you
0.60 <0.001

Willingness to Wait to Select Surgeon
57
• How long are patients willing to wait to select a
surgeon themselves compared to having their doctor
select a surgeon?
• What does this mean?
Surgeon reputation: good
2.0 <0.001
you
0.60 <0.001

Attributes and Levels
58
Attributes Levels
Surgeon
reputation
• Excellent
• Good
• Satisfactory
• Don’t know
Surgeon referral
• Selected by you
• Next available surgeon
• Selected by your doctor
Time to surgeon
consultation
• 1 month
• 6 months
• 12 months
• 18 months
Time to surgery
• 1 month
• 6 months
• 12 months
• 18 months
Time to hospital
• 1 hour of less
• More than 1 hour

Example DCE Choice Scenario
59
Attributes

Willingness to Wait to Surgeon
with an Excellent Reputation
 Patients are
willing to wait
~10 months
…to see a
surgeon with an
excellent
reputation (vs a
surgeon with a
good reputation)
- Marshall DA et al. Osteoarthritis and Cartilage, 2018

 Patients with the
worst pain are
willing to wait
~7 months
 Patients with the
least pain are
willing to wait
~12 months
…to select the
surgeon themselves
(vs being assigned
the next available
surgeon from a list)
Willingness to Wait to Select Surgeon
verses Next Available Surgeon
- Marshall DA et al. Osteoarthrits and Cartilege, 2018

62
preferences
3. Describe how patient preferences can be measured and how to
interpret the results from patient preferences studies
4. Identify ways in which patient preferences can
be applied in clinical practice for patients with
osteoarthritis
Learning Objectives

What is the Future for Arthritis Care
Informed by Patient Preferences ?
1) Support Patient Centered Care and Personalized
Medicine in Clinical Practice
2) Inform Clinical Practice Guidelines
3) Inform Regulatory Decisions about Therapies
63

64
Identify Preference Phenotypes of Patients with
RA for Treatment Based on Benefit-Risk Profiles
Prefer Triple Therapy
Risk averse (rare),
Cost sensitive, oral
Prefer anti-TNF
- Avoid bothersome side effects
Prefer anti-TNF
- Rapid onset of action
- Fraenkel L et al. Ann Rheum Dis, 2017.
Recognising
heterogeneity in
patient
preferences is
important for
choosing
treatment to
achieve best
outcomes for that
individual patient.

2) Using Patient Preferences to Inform Clinical
Practice Guidelines
65

Grading of Recommendations Assessment,
Development and Evaluation (GRADE)
66
Considerations in formulating guideline
recommendations (in addition to the quality of
the evidence):
• Tradeoffs between benefits and harms
• Uncertainty in the estimates of effects
• Values and preferences of benefits and harms from
those affected
• Translation of evidence into specific setting
• Resource implications
- GRADE working group. BMJ 2004.
√
√
√

Clinical Practice Guidelines: Patient
Preferences Can Differ from Guidelines
- Harrison M et al. BMJ Open 2017
67
- Hazlewood GS et al, Rheumatology, 2016; Hazlewood G et al, J Clin Epi 2018
Treatment preferences of patients with early rheumatoid arthritis:
• On average, patients were risk tolerant, supporting intensive
treatment approaches
• Two classes of patient identified:
a) Patients who were more averse to IV therapies and certain rare risks,
and
b) patients who were highly benefit-driven
Key Messages:
1. There was important heterogeneity in preferences that should
be considered in clinical treatment
2. In contrast to guidelines, many patients with early rheumatoid
arthritis may prefer triple therapy to other treatment options,
a) as initial treatment (78%) or after an inadequate response
to methotrexate (62%)

68
3) Patient Perspectives in Regulatory Decisions
Patient-Focused Benefit-Risk
Analysis to Inform Regulatory
Decisions Value in Health
Themed Issue, October, 2016
- Guest Editor Shelby Reed, Themed Issuue, Value in Health, Oct 2016
Patient-centered movement
Quantitative benefit-risk

Canadian Agency for Drugs
and Technology in Health
(CADTH)
Common Drug Review (CDR)
Process for Patient Input
69
CLINICAL
BENEFIT
ECONOMIC
EVALUATION
ADOPTION
FEASIBILITY
PATIENT INPUT
Drug Evaluation
Recommendations based
on
4 main criteria
https://cadth.ca/about-cadth/what-we-do/products-
services/cdr/patient-input
- Klein AV, Hardy S, Lim R, Marshall DA. Regulatory
decision-making in Canada – Exploring new frontiers in
patient involvement. Value in Health, 2016
Health Canada has an established
practice, albeit implicit and often ad
hoc, for including patient
perspectives in both operational and
policy-based regulatory decision-
making.
Value in Health Themed Issue,
October, 2016
Patient-Focused Benefit-Risk
Analysis to Inform Regulatory
Decisions

Summary of Patient Preferences in Arthritis
• Preferences measure risk benefit trade offs
• Good Research Practice guidance is available for
designing, conducting and analyzing preferences
• Clinical practice - preferences of patients can be
presented by distinct phenotypes to inform decisions
• Clinical guidelines- incorporating patients’ preferences
into clinical guideline development and
recommendations
• Regulatory Approval – Expect that evidence on
patient’s perspective will be part of the regulatory
approval process in the future.
70

71
Join us in Banff at our Next
DCE Workshop!
Applied Workshop – 3 days
September 9-11, 2019
Using Discrete Choice Experiments in
Health Economics: Theoretical and
Practical Issues

Thank you to colleagues and trainees and funders
Arthur J.E. Child Chair Foundation
Canada Research Chair Program
Thank you!
Deborah A Marshall
damarsha@ucalgary.ca
403-210-6377

What are Patient Preferences, How Do You Measure Patient Preferences, and How Do I Use Them?

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to What are Patient Preferences, How Do You Measure Patient Preferences, and How Do I Use Them?

Similar to What are Patient Preferences, How Do You Measure Patient Preferences, and How Do I Use Them? (20)

More from OARSI

More from OARSI (20)

Recently uploaded

Recently uploaded (20)

What are Patient Preferences, How Do You Measure Patient Preferences, and How Do I Use Them?