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Discrete Choice Experiments for Market Access

Discrete Choice Experiments for Market Access

What is Market Access?

When learning how to use Discrete Choice Experiments, first we have to look at the bigger picture. Market Access focuses on enabling patients to access treatments beneficial to their health. When a treatment finally survived the long and tedious process to get approval to go to the market, there is still no guarantee that that treatment will reach the patient to see the fruits of the time, energy, and money invested into that developing treatment. There could be multiple hurdles that stand in the way of getting the treatment to those that need them, such as issues with reimbursement, pricing strategies, and products and services of competitors.

Understanding the nature of those hurdles is essential to enter the market successfully. However, a problem arises: how will you try to understand those hurdles when the treatment has not yet entered the market? How will you try to understand why a patient would prefer a competitor’s treatment over that of yours? What will your pricing strategy be, and how elastic is that price?

This is where discrete choice experiments come into play.

    What are Discrete Choice Experiments?

    Discrete choice experiments (DCEs), also called discrete choice modelling, are becoming a more and more used survey technique from econometrics. The technique is used to find and understand the preferences for attributes that are (or are not) present in the treatment that you are providing and can be used to understand the decision-making process to predict choices between alternatives (also called profiles). These alternatives are mutually exclusive and can only be answered using pre-determined answers: stated preferences. Then, these stated preferences are bundled together in an alternative. Finally, based on the chosen alternative, the importance of each of the attributes is determined. This all may seem a bit complicated at first, but maybe clearer after the following example from a DCE performed by de Bekker-Grob et al.

    Example of a DCE used to compare treatments 

    One of the choices participants in the experiment were asked was to choose either one of three alternatives regarding influenza vaccination: have vaccination A, have vaccination B, or have no vaccination at all. Each of the alternatives has certain attributes, which in this case are effectiveness, the chance of serious side-effects, chance of mild side effects, protection duration, and absorption time. Each of these attributes constitutes of levels, which in the case of absorption time is either 4 weeks, 2 weeks, or none.

    The results of the DCE reveal a quantification of the attributes and attribute levels’ impact on the stakeholders’ decision-making process. For example, using DCEs to reveal the importance of certain attributes of vaccination may be especially relevant regarding the COVID-19 pandemic and possible future pandemics. In addition, revealing which attributes are regarded as more meaningful and impactful for deciding between alternatives could be very useful when considering treatment candidates in the research and development stages. Ultimately, DCEs are beneficial for Market Access since payers would choose your treatment over that of a competitor based on more preferable attributes.

    What is the difference between Conjoint & Discrete Choice Analysis?

    Aside from being able to determine whether to choose for treatment A or treatment B, DCEs can also be used in determining and describing the willingness to pay and price elasticity, which would make them very interesting and worthwhile to use concerning market access, especially since it is able to do so while the product or service has not even entered the market yet. You can do this by incorporating the price of a product or service as an attribute and using discrete choice modelling to calculate price impact. Additionally, using DCEs can give insight into how customers and other stakeholders value each of the features in a value proposition, which may be useful when deciding whether or not to invest and improve certain aspects of a treatment. When a DCE shows that an attribute is not as impactful as previously thought, it might be worthwhile investing in other attributes that are.

     

    Discrete Choice Experiments & Willingness to Pay

    Aside from being able to determine whether to choose for treatment A or treatment B, DCEs can also be used in determining and describing the willingness to pay and price elasticity, which would make them very interesting and worthwhile to use concerning market access, especially since it is able to do so while the product or service has not even entered the market yet. You can do this by incorporating the price of a product or service as an attribute and using discrete choice modelling to calculate price impact. Additionally, using DCEs can give insight into how customers and other stakeholders value each of the features in a value proposition, which may be useful when deciding whether or not to invest and improve certain aspects of a treatment. When a DCE shows that an attribute is not as impactful as previously thought, it might be worthwhile investing in other attributes that are.

     

    What is the difference between stated preference & revealed preference?

    The difference between stated preference (SP) and revealed preference (RP) is that the choice between alternatives in a discrete choice experiment using revealed preferences is observed in practice rather than surveyed. An often-made assumption using DCEs is that respondents accurately report the choice that they will be making. Respondents may not accurately predict their behavior or want to appear more socially desirable by choosing an alternative they would not choose in real-life. Suppose we again use an article using DCEs and vaccination behaviour as an example, this time from Lambooij et al.. In that case, we can see that a significant portion (26%) of respondents stated preference does not match their revealed preference. Observing revealed preferences can provide a more accurate result when calculating the impact of each of the level attributes in a DCE.

    However, in the context of market access, this may not always be possible because there is a lack of opportunity to observe, as the treatment may not yet be on the market. Ultimately, using stated preferences in DCEs is preferable in this context and is proven to be a powerful tool to be used in market access and research in general.

     

    How can you use Discrete Choice Experiments in Market Access?

    To summarize, Discrete Choice Experiments are a survey technique that originated in econometrics and is used to quantify how stakeholders value the attributes of a product or service. These attributes are evaluated using either stated preferences or revealed preferences, with the difference being that the choice in stated preferences is observed through a survey. In contrast, the choice in revealed preferences is observed in practice. In addition, discrete Choice Experiments differ from conjoint analysis in how the choice is presented, as the attributes are bundled together as an alternative in discrete choice experiments (hence the name ‘discrete choice’). Alternatively, these attributes are rated and evaluated independently in conjoint analysis.

    Discrete Choice Experiments are a powerful tool to use in a Market Access strategy because it enables you to quantify each of the attributes of your treatment. DCEs are a better representation of reality than conjoint analysis because treatments are often not modular, so bundling the attributes of that treatment makes more sense than evaluating them independently of one another.

    More generally, DCEs can be used  for valuing health and nonhealth outcomes, investigating trade-offs between health and non-health outcomes, and developing priority setting frameworks. Additionally DCEs allow to evaluate treatments and products that are not yet on the market where there is no information,  to predict uptake, willingness to pay and price elasticity.

    All in all, DCEs are a powerful tool to reveal and overcome certain hurdles in the process of market access.

    References

    de Bekker-Grob, E., Veldwijk, J., Jonker, M., Donkers, B., Huisman, J., & Buis, S. et al. (2018). The impact of vaccination and patient characteristics on influenza vaccination uptake of elderly people: A discrete choice experiment. Vaccine, 36(11), 1467-1476. doi: 10.1016/j.vaccine.2018.01.054

    Lambooij, M., Harmsen, I., Veldwijk, J., de Melker, H., Mollema, L., van Weert, Y., & de Wit, G. (2015). Consistency between stated and revealed preferences: a discrete choice experiment and a behavioural experiment on vaccination behaviour compared. BMC Medical Research Methodology, 15(1). doi: 10.1186/s12874-015-0010-5