In the field of Health Technology Assessment (HTA), understanding the preferences of patients, healthcare professionals, and other stakeholders is crucial for making informed decisions. One valuable tool for capturing these preferences is the Discrete Choice Experiment (DCE). This article aims to explore what a DCE is, its value in HTA, the main steps involved in implementing a DCE, and best practices when building a DCE. By following these steps, researchers can effectively elicit and analyze preferences, leading to better-informed decision-making processes.

What is a DCE?

A Discrete Choice Experiment (DCE) is a quantitative research method used to assess and measure preferences. It presents participants with a set of hypothetical choices between different health interventions or treatment options, each with different attributes, and asks them to state their preferred option. By analyzing these choices, researchers can determine which attributes are most important to patients and stakeholders and how they weigh the trade-offs between different attributes.

What is the value of DCE?

Patient Preference Studies (PPS) like DCE can be implemented in each step of the medical development process life cycle in various ways. For example, DCE provides valuable insights into patient needs during the discovery phase and can also help with trial design during the clinical development phase. Preferences can also be used to evaluate the value of healthcare interventions and during the HTA phase, stakeholders can use these insights to allocate resources more efficiently and effectively to develop treatments with preferred attributes.

Main steps to implementing a DCE:

Step 1. Define the research question: Clearly articulate the research objectives and the specific preferences to be measured. Identify the target population and relevant attributes that influence decision-making.

Step 2. Design the choice sets-choose the attributes and identify levels: Develop choice sets that represent the alternatives. Define the attributes and their levels based on a thorough literature review, expert input, and stakeholder engagement. Ensure that the combinations of attribute levels are realistic and representative of the decision context. Consider the appropriate number of choices to balance the respondent burden and statistical efficiency.

Step 3. Pilot testing: Before conducting the main study, it is essential to pilot test the DCE design. This helps identify any issues with the questionnaire, refine the attribute descriptions, and ensure that the choice sets are understandable and realistic to respondents.

Step 4. Sampling and data collection: Determine the appropriate sample size and sampling strategy based on the research question and target population. Consider the mode of data collection, such as online surveys, face-to-face interviews, or telephone interviews, based on the target population and available resources. Use the appropriate data collection method to minimize biases and maximize response rates.

Step 5. Data analysis and interpretation: Employ appropriate statistical techniques to analyze the data and estimate preference models. There are three main types of models to choose from. The first is a model to estimate preference weights conditional importance of attributes. The second model identifies groups with similar treatment preferences. The last one is an estimation of willingness to pay. After running the various models, interpret the results in the context of the research question. Provide clear and concise summaries of the findings, including the relative importance of attributes.

Best practices when building a preference study using DCE

• It is important to involve stakeholders, such as patients, caregivers, and healthcare professionals, in the design process to ensure that the research question and the attributes of interest are relevant and meaningful. This can be achieved through focus groups, interviews, or surveys.
• The experimental design should be simple, the number of attributes and levels should be kept to a minimum to avoid overwhelming the participants. Generally, the number of attributes to evaluate is between 5 and 8. The main categories of treatment attributes are: Benefits, Risk, and Treatment Modalities.

• The questions should be written in a way that reduces biases.  For example, there should be neutrality in phrasing the questions and each attribute should show up an equal number of times in the DCE.
• Clear instructions and guidance should be provided to the participants to ensure that they understand the purpose of the study and how to complete the DCE. It is important to explain the concept of trade-offs and the hypothetical nature of the choices.

Discrete Choice Experiments (DCEs) are valuable tools in the field of Health Technology Assessment (HTA) for eliciting and measuring preferences. By following the main steps outlined above and adhering to best practices, researchers can effectively design and implement preference studies using DCEs. The insights gained from DCEs contribute to evidence-based decision making, helping policymakers allocate resources and make informed choices that align with the preferences of patients, healthcare professionals, and other stakeholders.

EvidentIQ can provide support when conducting DCE studies. From the experimental design stage to implementation and reporting, EvidentIQ can customize a solution that can help you effectively execute your patient preference study. They offer best-in-class Real World Evidence (RWE) methodologies, such as DCE, for patient studies in multiple diseases and geographical areas thanks to their direct access to a global patient platform. Patient studies can focus on treatment preference, quality of life, value of health, disease/treatment burden, unmet needs, etc. EvidentIQ can help generate unique Real World Data (RWD) to significantly help life sciences customers support the value story of their product for HTA submission, pricing and reimbursement as well as their scientific communication within the clinical community.

Sources: 

https://www.youtube.com/watch?v=IPIkIXWOJ5g
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546533/#:~:text=A%20discrete%20choice%20experiment%20

https://yhec.co.uk/glossary/discrete-choice-experiment-dce/

https://bmjopen.bmj.com/content/11/3/e045803

https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-021-01647-z

Why collecting Real World Data is important to optimize clinical trials and get new medical product approved?

In clinical development, the traditional approach to demonstrate efficacy and safety of a new drug has always been in performing the traditional Randomized Clinical Trials (RCT) programs since this is considered by Regulatory Agencies as the best compromise to get sufficient, well-structured data from a subgroup of the future patient population in a reasonable time frame. However, these programs are highly cost-consuming and require the involvement of human resources (physicians, clinical research associates, etc.)

When a drug is assessed, Regulators and Health Technology Assessment (HTA) bodies require getting a far more extended set of data from a larger population and closer to normal life conditions. Having access to real life data enable to have a better understanding of disease and treatment burden, patient experiences, preferences to be able to establish how much the drug will cover unmet needs.

What is Real-World Data (RWD) and what are the different sources?

Real-world data is the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources, for example:

• Electronic health records (EHRs)
• Claims and billing activities
• Product and disease registries
• Patient-generated data including self-reported data entered by the patient or its relatives (e.g Patient-Reported Outcomes – PROs)
• Data gathered from other sources that can inform on health status, such as mobile devices.

What is Real-World Evidence (RWE)?

Real-world evidence is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD. RWE can be generated by different study designs or analyses, including but not limited to, randomized trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective). RWE is increasingly used in drug development and evaluation as shown by the recent guidelines published by the FDA.

What is new in July 2021 FDA guidelines?

Since 2016, after the publication of the 21st century Cure Acts, the FDA is taking into account RWD for the evaluation of new drugs/medical devices, mostly to monitor post-marketing safety or support extension of indications.

In July 2021, the FDA published guidelines to illustrate the interest of implementing RWD during clinical development phase and not only at the end of that lengthy phase. There are many approaches from including RWD in the RCT up to implement specific trials in parallel to the pivotal program. RWE is not a substitute for RCTs but should be seen as complementary, each type of study design providing information the other cannot.

Due to long processes during new drug/medical device development, pharmaceutical companies should evaluate the best approach to collect data as early as possible. Indeed bringing patient perspective into clinical projects can accelerate clinical trials, save costs and ensure the success of new products. .

For Medical Devices, the FDA is also relying on RWE to support their decisions and to evaluate risk/benefit.

EMA (European Medicines Agencies) is also working with European National Agencies on a strategic plan for 2023 road map in favor of the use of RWE in clinical development considering the benefit of having access upfront to real world data.

Some examples to Illustrate how FDA rely on RWE to support regulatory decision-making:

In July 2021, U.S. Food and Drug Administration approved a new indication for Prograf (tacrolimus) based on an observational, non-interventional study providing real-world evidence (RWE) of effectiveness. FDA approved Prograf for use in combination with other immunosuppressant drugs to prevent organ rejection in adult and pediatric patients receiving lung transplantation.

In a more recent time, FDA has decided for the Pfizer-BioNTech COVID-19 Vaccine to allow for use of a single booster dose, to be administered at least six months after completion of the primary series in specific subset of population: the decision was partly based on real-world data on the vaccine’s effectiveness over a sustained period of time provided by both U.S. and international sources, including the CDC (Centers for Disease Control and Prevention), the UK and Israel.

How Carenity can help you?

Carenity, part of the EvidentIQ group, offers Real-World Evidence solutions to collect and analyze self-reported patient information (ePROs, patient preference information using Discrete-Choice Experiment designs, e-diaries, etc.).

Carenity relies on its global digital platform that engages more than 500,000 patients and caregivers in Europe and North America to conduct prospective non-interventional studies (cross-sectional and longitudinal designs). Theses observational studies can generate meaningful information about quality of life, disease and treatment burden, product effectiveness, patient preferences, etc.

Carenity supports its Life Sciences Industry clients at each step of the clinical development to ensure the success of patient recruitment and retention thanks to patient-centric decision-making:

• Target Product Profiles (TPP) definition
• Study design optimization and protocol evaluation
• Study feasibility assessment
• Endpoint optimization
• Test of patient materials (Informed Consent Form, Information notice, etc.) and patient services

Carenity also offers PatientLive®, a self-service platform to collect real-time patient insights. This unique solution allows to get fast answers from patients and caregivers and make better decisions via a quick poll (1-5 questions).

Contact us for more information: pro@carenity.com

Bibliography :

https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/submitting-documents-using-real-world-data-and-real-world-evidence-fda-drugs-and-biologics-guidance

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-real-world-evidence-support-regulatory-decision-making-medical-devices

https://www.fda.gov/drugs/drug-safety-and-availability/fda-approves-new-use-transplant-drug-based-real-world-evidence

https://www.fda.gov/news-events/press-announcements/fda-authorizes-booster-dose-pfizer-biontech-covid-19-vaccine-certain-populations

https://www.ema.europa.eu/en/about-us/how-we-work/big-data