What can hypothetical choices tell us about unobservable behaviours?

Nicolas KRUCIEN, Verity WATSON, Mandy RYAN

Health Economics Research Unit University of Aberdeen

10th World Congress in Health Economics

Dublin, 13/07-16/07/14

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Context • Growing evidence that participants to a DCE do not process the

information as suggested by the RUM model – ANA: Some attributes are excluded from the utilities

computation and comparison – RRM: Utilities comparison would mainly based on the

direct comparison of some attributes • Accounting for these behavioural phenomenon can lead to

qualitatively different DCE results • Models have been developed to account for these departures

from the standard RUM model, but weak theoretical foundations

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Objective

• To improve modelling of preferences in a DCE by allowing respondents to use more flexible information processing strategies [IPS] (‘as if’ approach).

• Two main propositions: 1) Limited Attention instead of classical non-attendance 2) Co-existence of several IPS within the same choice

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Method: Proposition 1

• Limited attention – Inspired by the Rational Inattention (RI) theory (Sims, 2003)

• Main components of the RI framework: – Agents have limited amount of cognitive resources they can

allocate to the decision problem – They have to decide which pieces of information (attributes)

is worth looking at (Comparison of costs and benefits) – The final choice (2nd stage) is conditional upon information

trade-offs made at the 1st stage – Agents still act rationally (Optimisation under constraint)

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Method: Proposition 1

• “Impossible” to specify a cost function without additional information on attributes' valuation by the respondents

• Respondents simplify the information processing by ‘guessing’ about attributes’ values

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Method: Proposition 1 • Different ‘guessing’ processes are compared: • Look at one alternative and use the observed values to infer

those of the remaining alternative – Probability of similarity: {0%, 10%, 20%}

• Look at one alternative and make ‘simple’ guesses about values of the other alternative – Actual values are replaced by expected values based on the

assumption that levels are equally likely • Look at one alternative and make ‘intelligent’ guesses about

the values of the other alternative – Actual values are replaced by expected values that took into

account the probability of each level to occur so far

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Method: Proposition 2 • Random Regret Minimisation (RRM) (Chorus et al, 2008)

– Close to RUM in terms of GoF but still leads to important differences (predicted probabilities)

– Agents’ decision making is based on attributes comparison (Low order) vs. alternatives comparison (High order)

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Attribute Alt [A] Alt [B] Alt [C]Cost (in £) 10 5 5

Time (in min) 30 20 30

U(A) U(B) U(C)

BR(C)

BR(C)

Method: Proposition 2 • Choice of RUM/RRM as an IPS is made for each attribute

separately depending on several factors (importance; format; uniqueness)

• 16 ‘hybrid’ models are estimated and compared to identify which attribute is best accounted for by either RUM/RRM

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RUM RRM

DCE

• Secondary data analysis • Patients' preferences for

the role of the pharmacist in management of drug therapy • 4 attributes:

– Travel + Waiting time to GP [3 levels] – Travel + Waiting time to pharmacy [3 levels] – Chance of receiving best treatment [3 levels] – Cost [4 levels]

• 16 tasks including 3 alternatives {A, B, SQ} • 204 respondents (3,265 observations)

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Results • Choice proportions • Limited Attention

Remark: Best ANA model “Pharma time” omited: LL= -2811.1

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Utility RegretLOOK [A] INFER [B] (Rate: 0%) -2800.6 -2804.9LOOK [A] INFER [B] (Rate: 10%) -2805.3 -2809.6STANDARD -2811.1 -2813.1LOOK [A] INFER [B] (Rate: 20%) -2821.9 -2825.4LOOK [A] ASSUME DYNAMIC [B] -2834.6 -2836.5

Information processingLog-Likelihood

Table. Comparison of the different 'guessing' processes (Top 5)

A B SQ31.3% 12.0% 56.7%

Results • Hybrid IPS (+ “Look [A] Infer [B] at 0%”)

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GP time Pharma time Chance Cost LogLikR R U R -2793.6R U U R -2793.7U R U R -2795.1U U U R -2795.2R U R R -2797.2

Table. Comparison of the different hybrid RUM-RRM models (Top 5)

Results

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Beta 0.7184 * 0.4807 * 0.6058 * 0.5988 *SE 0.0528 0.0372 0.0522 0.0541

Beta -0.0139 * -0.0087 * -0.0133 * 0.0090 *SE 0.0014 0.0009 0.0014 0.0009

Beta -0.0001 -0.0003 0.0070 * -0.0049 *SE 0.0020 0.0013 0.0024 0.0016

Beta 0.6836 * 0.4684 * 0.6864 * 0.6824 *SE 0.0363 0.0249 0.0388 0.0388

Beta -0.0624 * -0.0399 * -0.0722 * 0.0513 *SE 0.0047 0.0030 0.0052 0.0036

LogLik -2811.1 -2813.1 -2800.6 -2793.6

Pharma time

Chance

Cost

AttributeTable. Comparison of the different discrete choice models

(1): Regret={GP time; Pharma time; Cost} - Utility={Chance}

RUM RRM RUM - LA HYBRID - LA (1)

ASC_SQ

GP time

Conclusion • Evidence that participants to a DCE make some assumptions

about the content of the alternatives – Is ANA generated by the researcher?

• In line with regret theory (Loomes & Sugden, 1982), respondents’ preferences are partially based on the anticipated performance of a considered option + other alternative(s) – Why is it true for only some attributes?

• Application of RI theory to DCM choice modelling is an interesting research avenue (raising several methodological issues) – Use of eye-tracking technology to collect data about respondents'’

“information- trade-offs among the attributes

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Thank you for your attention

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