Patient preference studies - a practical introduction

Patient preference studies measure treatment trade-offs quantitatively. Regulators, HTA bodies, and payers increasingly require them.

What patient preference studies are, when they are required, what regulators expect, and how to plan one that meets those expectations.

Ben White, 07.07.2026

What is a patient preference study?

A patient preference study measures the relative importance of treatment attributes to patients and estimates the trade-offs between them. How much additional risk of side effects will a patient accept for improved efficacy? How much is a more convenient administration schedule worth? Patient Preferences, or "The Voice of the Patient" are being increasing integrated within clinical trials or product lifecycles. The data guides development and supports submissions.

DCEs are the best-of-breed method to achieve this as they are designed to measure quantitatively the value of trade-offs like risk and efficacy, in a familiar setting with academic rigour. They are one of few methods endorsed by regulatory bodies, such as the FDA.

When and why you need a patient preference study

Preference data is now expected at multiple stages of drug development. Early in development it informs trial design and endpoint selection. At regulatory submission it supports benefit-risk assessment and labelling. At HTA it feeds cost-effectiveness analysis and coverage decisions.

The FDA's 2019 Patient Preference Information guidance, NICE's patient involvement requirements, and the European Patients' Academy guidance all explicitly reference quantitative preference methods. Submitting without preference data is an increasingly difficult position to defend.

Beyond regulatory requirements, preference data identifies which treatment attributes drive patient choice and how much patients value improvements in each - directly relevant to pricing, positioning, and market access.

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Planning a patient preference study

Step 1: Define the decision. The decision context - regulatory, HTA, or commercial - determines the attributes, target population, required precision, and timeline. A study designed for an FDA benefit-risk submission looks very different from one supporting a market access negotiation.

Step 2: Qualitative research first. Quantitative preference studies require qualitative precursor work - interviews or focus groups with patients - to identify the attributes that actually matter. Studies that skip this step identify the wrong attributes.

Step 3: Choose your method. For most health preference applications a DCE is appropriate. It produces trade-off estimates and WTP values that are statistically defensible and interpretable by regulators. Rating scales and BWS are defensible for early-stage exploratory work.

Step 4: Plan recruitment early. Incidence rates for specific patient populations are frequently lower than anticipated. Ethics approval timelines are long. Both need to be confirmed before the study design is finalised.

Worked example - rare disease patient preference study

A biotech company developing a treatment for a rare autoimmune condition conducts a patient preference study to support an FDA benefit-risk submission. Four qualitative focus groups with patients identify five key attributes: reduction in disease flares, fatigue, injection site reactions, monthly cost, and frequency of monitoring visits.

The quantitative DCE is conducted with 150 patients recruited through patient advocacy organisations with confirmed diagnosis. The study produces utility estimates for each attribute level and WTP estimates that are incorporated into the submission. The FDA accepts the evidence and references it in the drug label.