This project seeks to elicit the public’s preferences for different features of a genomic test to sequence advanced solid cancer tumours. Understanding the relative preferences for various attributes of targeted testing are useful for determining the value of sequencing approaches, and informing technology adoption decisions. A discrete choice experiment (DCE) survey was designed to assess the preferences of members of the Australian general public for targeted sequencing in advanced cancer. The survey presented respondents with 12 questions in which they had to choose between two unlabelled tests (Test A and Test B). Tests were specified in terms of five attributes: time to receive the test result; cost of the test; likelihood that the test result will lead to a change in treatment; length of time health care professionals spend describing the test; and type of health care team who explains the test result. Respondents were sampled from an online panel and also completed questions related to demographic and socio-economic factors, experiences of cancer and familial history. We found that cost, timeliness, expertise/location and likeliness of changing treatment regimes were identified as attributes of genomic sequencing that are most valuable to a sample of the public. These results will ultimately be compared with the results of an ongoing DCE being conducted with patients with advanced cancer who are undergoing sequencing. Author(s) and affiliation(s): Paula Lorgelly (OHE), Grace Hampson (OHE), James Buchanan (Oxford), Melissa Martyn (MGHA), Jayesh Desai (PeterMac), Clara Gaff (MGHA), and iPREDICT MGHA Flagship collaborators Conference/meeting: EuHEA 2018 Location: Maastricht, the Netherlands Date: 12/07/2018

1. 12-13 June 2018
The value of targeted sequencing in
advanced cancer:
DCE to elicit the public’s preferences
Paula Lorgelly*, Grace Hampson, James Buchanan,
Melissa Martyn, Jayesh Desai, Clara Gaff,
iPREDICT MGHA Flagship collaborators
*Office of Health Economics & King’s College London

2. DCE genomic cancer - EuHEA 2018
Background
• Melbourne Genomics Health Alliance (MGHA) Solid Cancers
Flagship
• iPREDICT (Incorporating complex PRofiling of patients to Enroll onto
molecularly-DIrected Cancer Therapeutics)
• Aims to generate evidence to inform the implementation of
genomic medicine into clinical care for patients with advanced
solid cancers within the health care system
• Health economic component assesses cost-
effectiveness and preferences to help provide
evidence for its implementation

3. DCE genomic cancer - EuHEA 2018
Objective
• To elicit the public’s preferences for different features of a
genomic test to sequence advanced solid cancer tumours
• [Ongoing work eliciting patient preferences]
• Relative preferences for different attributes of targeted testing
will be useful for determining the value of sequencing
approaches, and informing technology adoption and service
design decisions

4. DCE genomic cancer - EuHEA 2018
Current evidence base
• Search literature on preferences for genomic tests, in
particular relating to cancer, wrt patients and/or the public
• The search was undertaken in Medline in May 2018,
supplemented through Google Scholar and by searching the
reference lists of included studies
• Identified 10 papers, 4 specific to cancer, but not in advanced
cancer and none in the Australian setting
• Two most relevant: Buchanan et al. (2016) and Najafzadeh et
al. (2013), they explore relative preferences across various
attributes of genomic testing for existing (or hypothetically
existing) conditions

5. DCE genomic cancer - EuHEA 2018
Reference Aim(s) Population Attributes and levels Main reported conclusions
Buchanan et
al. 2016
To evaluate patient
preferences for
genomic testing in
the context of
chronic lymphocytic
leukaemia (CLL)
CLL patients in
the United
Kingdom (UK)
N=219
1) Time to receive the test result: 5 days; 8 days; 11
days; 14 days
2) Cost of the test: £130, £260, £400, £600
3) Ability of the test to predict who will not
respond to the usual chemotherapy treatment: X /
100 patients who will not respond (X=30;50;70;90)
4) Test reliability: X / 100 tests provide an incorrect
result (X=2;4;6;8)
5) Length of time clinicians spend describing the
test: 5 min; 10 min; 15 min; 20 min
6) Type of clinician who explains the result: general
practitioner; specialist nurse; junior hospital doctor;
consultant hospital doctor
Patients prefer tests that are more
effective, more reliable, cheaper and
which return results quickly. Patients
prefer to receive these test results in a
15-min appointment with a clinician
who is perceived to be a CLL expert.
Genomic testing was associated with
higher utility than genetic testing.
Najafzadeh
et al. 2013
To elicit preferences
for different
attributes of a
hypothetical
genomic test for
guiding cancer
treatment
Two samples:
General public
in Canada,
N=1,058;
Current or
former cancer
patients in
Canada, N=38.
1) Untreated responders: 5%; 20%; 35%; 50%
2) Unnecessary treatment of non-responders: 5%;
20%; 35%; 50%
3) Severity of side effects: severe; moderate; mild
4) Likelihood of side effects: 5%; 50%; 95%
5) Test turnaround time: 2 days; 7 days; 12 days
6) Test procedure: mouth swab; blood sample;
tumor biopsy; bone marrow biopsy; liver biopsy
7) Test cost: $50; $500; $1000; $1500
Change in severity, likelihood of side
effects and test procedure have the
largest influence on the public’s
decision to use genetic testing.
Sensitivity of the test had a larger
influence on patients’ decisions.
Patients and the public have different
perceptions about various aspects of
genomic testing to guide cancer
treatment.

6. DCE genomic cancer - EuHEA 2018
Method – Discrete Choice Experiment
• 12 choice sets in which they had to choose between two
unlabelled tests (Test A and Test B)
• Tests were specified in terms of five attributes,
between two and five levels
• D-efficient experimental design was produced for the
DCE that incorporated estimates of coefficient priors
derived from a previous DCE and a pilot of this DCE
• Respondents were sampled (representatively to reflect a patient
population) from an online panel, half had experience of cancer
• Survey also included questions on demographics and socio-
economic factors

7. DCE genomic cancer - EuHEA 2018
Attributes

8. DCE genomic cancer - EuHEA 2018
Example DCE question

9. DCE genomic cancer - EuHEA 2018
Analysis
• Mixed logit regression analysis [mixlogit]
• Iterative process to select the appropriate number of Halton draws
for the estimation process, best fit given AIC & BIC [nrep(500)]
• Next determined which parameters will be
fixed and which will be random; random
parameters those with SD that were
significant
• Adjusted the regression model to allow for
correlation between attributes
• Estimated MRS (including WTP) and tested
interactions with own/family experience

10. DCE genomic cancer - EuHEA 2018
Sample
• Survey conducted Feb-March 2018
• CINT, online panel survey company
• Target sample of 125 members of general public & 125 members of
general public with own/family experience of cancer
• 33% response rate, 254 completed with 512 dropped out, 163
screened out as quotas filled [cancer experience, state, age]
• 128 (of 254) with own/family experience of cancer

11. DCE genomic cancer - EuHEA 2018
Demographics of the sample
Age N % Children N %
18-30 14 5.51 No 81 32.14
31-40 26 10.24 Yes 171 67.86
41-50 48 18.9 number of children (mean) 2.29
51-60 68 26.77 Qualifications
61-70 55 21.65 Year 11 or below 44 17.39
71+ 43 16.93 Year 12 or equiv 27 10.67
Gender Certificate 53 20.95
Female 144 56.69 Dip/Adv Dip 46 18.18
Male 110 43.31 Bachelor 52 20.55
Income (AUD) Grad dip/cert 14 5.53
<10,000 10 3.94 Post-grad 17 6.72
10k-30k 37 14.56 State
30k-50k 53 20.87 ACT 4 1.57
50k-70k 46 18.11 NSW 86 33.86
70k-90k 36 14.17 NT 1 0.39
90k-120k 27 10.63 SA 19 7.48
>120K 33 12.99 TAS 2 0.79
no answer 12 4.72 VIC 82 32.28
Marital status WA 23 9.06
Married 136 53.75 QLD 37 14.57
De facto 19 7.51
Divorced/separated 40 15.81
Widowed 13 5.14
Never married 45 17.79

12. DCE genomic cancer - EuHEA 2018
Ranking
• Pre-DCE
• Asked to rank attributes from the most to least important
1. Cost of the test
2. Time to receive the test
3. Likelihood of changing treatment
4. Length of time taken to explain
5. Who explains the results
• Note for those with own/family experience cost and time both
most important
• Post DCE: ranking did not change (also for own/family experience)

13. DCE genomic cancer - EuHEA 2018
Informal test of rationality
• ‘Test’ question of rational respondents
• A (shortest time to receive results; lowest cost; greatest
likelihood of changing treatment; longest time spent explaining
the test; specialist treatment team) vs B
• Note specialist vs local preference not clear
• 94.5% of sample preferred A
• Kept the 5.5% in the sample as per Lancsar
and Louviere (2006)

14. DCE genomic cancer - EuHEA 2018

15. DCE genomic cancer - EuHEA 2018
-1000
-800
-600
-400
-200
0
200
400
600
800
Time
Change0
Change1
Change2
Change3
Explain0
Explain1
Explain2
Who
WTP($AUD)
Willingness to Pay
Willing to pay $550 to receive the results in 2 weeks rather than 6 weeks
Willing to pay $220 to have a specialist explain the test rather than
local oncologist

16. DCE genomic cancer - EuHEA 2018
WTP with own/family experience interactions
-1000
-800
-600
-400
-200
0
200
400
600
800
Time
Change0
Change1
Change2
Change3
Explain0
Explain1
Explain2
Who
interactTimeFH
interactChange0FH
interactChange1FH
interactChange2FH
interactChange3FH
interactWhoFH
WTP($AUD)
No independent effect of own/family cancer, experience plays minimal role in preferences

17. DCE genomic cancer - EuHEA 2018
Qualitative comments
• Insight on attributes
• “Advanced cancers are often not curable
hence other minor considerations (such as
time to receive results) would not be given
much weight.”
• “Cost is the main factor for aged pensioners
and getting treatment ASAP is imperative as
well”
• “It would be difficult to be objective if it was
definitely my cancer prognosis in question.”
• “The factors that I looked at each time
changed with each question. Sometimes it
was the length of time that I would have to
wait for results, other times it was the
likelihood of a change in treatment. There
was no one factor that affected my decision.”
• Questionnaire design
• “It was not simple to work with and had too
many options and changes of method of
answering.”
• “It doesn't take into account that everyone
has a choice; not a choice out of two
possible answers and the answer might not
be any of the ones given in this survey.”
• “A survey that really gets you thinking. I
feel the best treatment plans with the best
results would come from medical
specialists.”

18. DCE genomic cancer - EuHEA 2018
Next steps
• Evaluate dominance of preferences
• Test for preference heterogeneity by fitting alternative
functional forms, including latent class models
• Additionally explore role of income (mixed private-public system)
and urban-rural location
• Estimate the utility/WTP of different testing scenarios,
compare this with potential reality of delivering a genomic
testing service in Victoria, Australia

19. DCE genomic cancer - EuHEA 2018
Summary
• Cost, timeliness, expertise/location and likeliness of changing
treatment regimes were identified as attributes of genomic
sequencing that are most valuable to a sample of the public
• Results will ultimately be compared with the results of an ongoing
DCE being conducted with patients with advanced cancer who
are undergoing sequencing
• Comparison between the preferences of patients and the public
(i.e. potential patients) useful for HTA, provide input to debate on
societal preferences vs patient preferences (i.e. role of
experience) for decision making

20. DCE genomic cancer - EuHEA 2018
Acknowledgements
• This study would not be possible without responses from the
general public. We would additionally like to thank colleagues at
the Alliance and OHE who piloted an earlier version of the DCE.
• The study received funding from the Melbourne Genomic Health
Alliance (the Alliance)

21. DCE genomic cancer - EuHEA 2018
Thank you
To enquire about additional information and analyses, please contact Paula Lorgelly –
plorgelly@ohe.org
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