WTP estimation for health outcomes in patient preference studies

WTP estimates for health outcomes - reduced side effects, improved efficacy, better quality of life - have certain benefits over TTO and QALYS as an output output of patient preference DCE studies.

This article explains how to estimate WTP for health outcomes in patient preference studies, interpret the results in the context of benefit-risk assessment, and present them for regulatory submissions.

Ben White, 07.07.2026

What WTP means in a health preference context

In health preference studies, WTP for an improvement in a health outcome has a specific interpretation: it is the amount of money a patient would give up - in the form of higher out-of-pocket costs, insurance premiums, or foregone income - to achieve the improvement. Alternatively, WTP can be expressed as the acceptable increase in risk of an adverse outcome to achieve a specified benefit.

The latter - the benefit-risk trade-off - is the most policy-relevant output of patient preference studies. How much additional nausea risk would a patient accept for a 15% improvement in response rate? How much additional injection site reaction risk for a more convenient dosing schedule? These are the questions that patient preference studies are designed to answer, and WTP is the metric that operationalises the answer.

Using WTP for benefit-risk assessment

The FDA's patient preference guidance explicitly references the benefit-risk framework as the primary use case for patient preference information. WTP estimates from DCE studies inform two aspects of benefit-risk assessment: the relative importance of different benefits and risks; and the acceptable trade-off rates between benefits and risks.

For HTA submissions, WTP estimates from patient preference studies can be combined with clinical evidence to calculate quality-adjusted measures of patient benefit that inform cost-effectiveness analysis. NICE has used patient preference evidence from DCE studies in technology appraisals for oncology and rare disease treatments.

SurveyEngine's Apollo integration produces WTP estimates with confidence intervals for all attribute parameters, formatted for inclusion in regulatory and HTA submission dossiers.

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Estimating and presenting health outcome WTP

Step 1: Specify your attribute coding. WTP estimates depend critically on how attributes are coded. Continuous attributes (probability of nausea, HbA1c reduction) give WTP per unit change. Binary attributes (hair loss yes/no) give WTP for the full attribute change. Dummy-coded categorical attributes give WTP for each level relative to the base.

Step 2: Choose preference space or WTP space. For regulatory submissions, WTP space estimation is preferred because it provides more reliable confidence intervals. For exploratory analysis, preference space is often adequate.

Step 3: Calculate benefit-risk trade-offs. Express WTP in clinically meaningful terms: the acceptable increase in probability of severe adverse effect (expressed in percentage points) for a specified improvement in efficacy. This is: -(WTP_efficacy / WTP_AE) where WTP is measured in probability units.

Step 4: Apply minimum detectable WTP thresholds. For each attribute, calculate the minimum WTP estimate that is statistically distinguishable from zero. Attributes with WTP estimates below this threshold are not reliably influencing patient choice.

Step 5: Segment by patient characteristics. If your sample size allows, estimate WTP separately for patient sub-groups (treatment-naive vs treatment-experienced, disease severity sub-groups). Heterogeneity in WTP across sub-groups is both scientifically interesting and clinically relevant.

Worked example - benefit-risk trade-off in rheumatology

A patient preference study for a rheumatoid arthritis treatment produces WTP estimates: ACR50 response improvement 10 percentage points = WTP £28/month; probability of serious infection increase 1 percentage point = WTP -£42/month (i.e. patients would need a £42/month cost reduction to accept a 1 pp increase in serious infection risk).

The benefit-risk trade-off: patients are willing to accept a 0.67 percentage point increase in serious infection risk per 10 percentage point improvement in ACR50 response (28/42 = 0.67). This trade-off rate informs the FDA benefit-risk framework, suggesting patients weigh a 10pp efficacy gain roughly equivalent to a 0.7pp safety cost.