Concept Architecture
Concept
Theoretically, Choice-Based Conjoint (CBC) is a stated preference method used to estimate individual preferences by observing choices between hypothetical alternatives characterised by multiple attributes. It is based on random utility theory, which assumes that individuals choose the alternative that provides the greatest utility. The method exists to quantify the relative importance of attributes and estimate trade-offs between competing characteristics of healthcare interventions.
Mathematically, Choice-Based Conjoint represents the utility of each alternative as a function of its attributes and estimates preference parameters using discrete choice models. The mathematical framework predicts the probability of selecting each alternative based on its utility relative to competing alternatives.
In practice, Choice-Based Conjoint is implemented by presenting respondents with repeated choice tasks, estimating utility coefficients using conditional logit, multinomial logit or mixed logit models, and deriving measures such as willingness-to-pay, attribute importance and predicted choice probabilities. It is widely used in health economics to elicit patient, clinician and public preferences for healthcare interventions.
Purpose
Used to estimate preferences for healthcare interventions, quantify attribute trade-offs, derive willingness-to-pay estimates, inform health technology assessment, and support healthcare decision-making.
Mathematical Formulae
Primary Formula
U?? = V?? + �??
Where:
- U?? = Utility of alternative j for individual i
- V?? = Systematic utility
- �?? = Random error term
Supporting Formulae
Systematic utility:
V?? = ??x??? + ??x??? + ? + ??x???
Choice probability (multinomial logit):
P?? = exp(V??) / ????? exp(V??)
Related Mathematical Methods
- Random Utility Theory
- Conditional Logit Model
- Multinomial Logit Model
- Mixed Logit Model
- Maximum Likelihood Estimation
Example
A Choice-Based Conjoint study asks patients to choose between alternative medicines differing in:
- Monthly cost
- Treatment effectiveness
- Risk of adverse effects
- Dosing frequency
Responses from 1,000 participants are analysed using a multinomial logit model to estimate utility coefficients for each attribute. The estimated coefficients are then used to predict treatment preferences and willingness-to-pay for improvements in effectiveness or reductions in adverse effects.
Excel Implementation
| Function | Example Formula | Health Economics Application |
|---|---|---|
| SUMPRODUCT | =SUMPRODUCT(B2:E2,$B$10:$E$10) | Calculate systematic utility from estimated attribute coefficients. |
| EXP | =EXP(F2) | Calculate exponential utility for the logit model. |
| SUM | =SUM(G2:G5) | Calculate the denominator for choice probabilities. |
| Division | =G2/$G$6 | Calculate predicted choice probabilities. |
VBA (Optional)
Automate the preparation of choice experiment datasets and export formatted data for discrete choice modelling software.
Sources
- Louviere JJ, Hensher DA, Swait JD. Stated Choice Methods: Analysis and Applications. Cambridge University Press.
- Train KE. Discrete Choice Methods with Simulation. Cambridge University Press.
- Bridges JFP, Hauber AB, Marshall D, et al. Conjoint Analysis Applications in Health: A Checklist. Value in Health.
- ISPOR Conjoint Analysis Good Research Practices Task Force Reports.
Related Concepts (2)
Frequently Asked Questions (6)
What is choice-based conjoint analysis?
A form of conjoint analysis in which respondents choose their preferred option from a set of alternatives rather than rating each one individually.
Source: Louviere, Hensher & Swait 2000
Why does choice-based conjoint ask for a choice rather than a rating?
Choosing between options forces a trade-off, since taking one means giving up the others, whereas rating each option separately allows a respondent to score everything highly and reveals nothing about what they would sacrifice. The choice task also resembles the decisions people actually make, which supports the claim that the answers describe real preferences. It has the further advantage of resting on an established theory of how people choose, which supplies the statistical model used to analyse the responses.
Source: Louviere, Hensher & Swait 2000
How is a choice-based conjoint experiment designed?
The service or treatment is described by a small number of attributes, each taking a defined set of levels, and respondents are shown sets of hypothetical alternatives differing in those levels. The combinations presented are selected by an experimental design rather than at random, so that the effect of each attribute can be estimated separately and the design extracts as much information as possible from a limited number of questions. Including a no-treatment or opt-out option allows the value of the service as a whole to be estimated rather than only the relative value of its features.
Source: Lancsar & Louviere 2008
How are choice-based conjoint responses analysed?
The analysis assumes each respondent attaches a value to each attribute level, chooses the option with the highest total, and that an unobserved random element accounts for departures from that rule. Estimating the model yields a weight for each attribute level, showing how much it contributes to the attractiveness of an option. Where cost is one of the attributes, dividing the weight on another attribute by the weight on cost expresses that attribute in monetary terms, which is how willingness to pay is derived.
Source: McFadden 1974
What is choice-based conjoint used for in health?
It is used to value the features of a service, including waiting time, travel distance, continuity and mode of consultation, which no clinical outcome measure captures. It supplies monetary values for benefits where a cost-benefit framework is being used. It establishes patient preferences between treatments differing in effectiveness and side effect profile, informing shared decision making and appraisal. It is also used to examine the job characteristics that influence where health professionals choose to work.
Source: de Bekker-Grob, Ryan & Gerard 2012
What threatens the validity of choice-based conjoint results?
Respondents frequently ignore some attributes altogether and decide on one or two, which the standard model assumes they do not, and this distorts the estimated weights. The choices are hypothetical and carry no consequence, so stated willingness to pay tends to exceed what people actually pay. Tasks with many attributes exceed what respondents can process, and they simplify in ways the design does not anticipate. Evidence that the choices predict real behaviour remains limited, which is the underlying concern.
Source: Bridges et al. 2011
Trust Record
Verified by Dr Darrin Baines
British health economist
Professional identity: darrinbaines.org
Verification date: 31 Jul 2025
Content version: 1.0.0
Canonical Identity
- Term code
- HE-EE-CBA-009
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