What is a discrete choice experiment (DCE) in healthcare?
Discrete choice experiments (DCEs) method has been very popular in the marketing and economics field. In the healthcare industry, this technique is used for decision-making by healthcare providers and obtaining preferences. The participants are asked hypothetical questions with a choice of 3-5 answers. This quantitative method allows healthcare decision-makers to reach a conclusion on a particular treatment which may have multiple alternatives with various benefit-risk profiles.
For example, a pharma company is planning to launch a painkiller and carries out a survey with participants being those who regularly take painkillers. The set of questions can be whether they like their painkillers to be in which form (tablet, injectable or liquid), how much time it takes for meds to start working, how many doses they require, and so on.
Why use a discrete choice experiment?
It equally helps patients, payers, and other stakeholders to get insights on various attributes and attribute levels. Discrete choice experiments (DCEs) consist of hypothetical questions that are designed to mimic real-world situations and decisions. The findings from this tool can be used to measure preferences and improve decision making.
How are discrete choice experiments conducted in healthcare?
DCEs are widely used in health economic modeling to obtain patient/healthcare providers and stakeholders’ preferences. Participants are recruited through patient registries, healthcare providers data, or online platforms. DCEs are conducted through various interviews of key informants, literature reviews. Starting with a questionnaire, a checklist is conceptualized of attributes and levels. Participants are told to state their preferences for a particular product, therapy or policy.
What are the benefits of discrete choice experiment?
Through DCE, healthcare providers can quantify preferences, understand preferences among various patient subgroups. Discrete choice experiment is a valuable method to describe a product, therapy or policies’ attributes. They provide more information on preferences and majorly contribute to outcome in economics evaluation. DCEs are among the most preferred valued methods in healthcare evaluation, knowing patient preference, assessing diagnostic services, or assessing different routes of administration of drugs.
How do you analyze a discrete choice experiment?
Researchers use statistical models for interpretation of data collected. The most common methods used for analyzing discrete choice experiments are multinomial logit model, conditional logit model, nested logit model, mixed logit model. The aim of the analysis is to obtain and understand patients’ preferences and variations, medical workers’ preferences, derive utility, conceptualize the choice process and compare the advantages/disadvantages of the stated preferences.
What are some common challenges in conducting a discrete choice experiment?
- Cognitive Burden
- Complexity of choices
- Generalizability
- Ethical considerations
- Cultural relevance
- Participant Recruitment
- Various external factors
How do DCEs contribute to HEOR?
DCEs help healthcare providers and researchers in understanding and assessing patients’ preferences and their choices. This helps in gaining insights on attributes of preferred drugs, treatments, efficacy, cost and route of administration. Due to heterogeneity, a particular group of patients may prefer a particular attribute while another group may prefer some other. DCEs help uncover such differences and allow informed decision making.
DCEs can integrate health economic modeling and provide cost-effectiveness analyses. This greatly helps in understanding the tradeoffs between different treatment attributes, have a positive impact on health economics and improve patient outcomes. Pharma companies utilize DCEs methods to elicit patient preferences about their products, therapies or devices.
Thus, discrete choice experiment methodology plays an important role in HEOR by providing data on patient preferences, leading to patient-centric and cost-effective drugs, therapies and policies.
