The impact of reproductive issues on preferences of women with relapsing multiple sclerosis for disease modifying treatments

Background Relapsing–remitting multiple sclerosis (RRMS) is an incurable disease characterised by relapses (periods of function loss) followed by full or partial recovery, and potential permanent disability over time. Many disease-modifying treatments (DMTs) exist that help reduce relapses and slow disease progression. Most are contraindicated during conception/pregnancy and some require a discontinuation period before trying to conceive. Although around three-quarters of people with RRMS are women, there is limited knowledge about how reproductive issues impact DMT preference. Objective The aim of this study was to measure the preferences for DMTs of women with RRMS who are considering pregnancy. Design An online discrete choice experiment (DCE). Methods Participants chose between two hypothetical DMTs characterised by a set of attributes, then indicated if they preferred their choice to no treatment. Attributes were identified from interviews and focus groups with people with RRMS and MS professionals, as well as literature reviews, and included the probability of problems with pregnancy, discontinuation of DMTs, and breastfeeding safety. In each DCE task, participants were asked to imagine making decisions in three scenarios: now; when trying to conceive; and when pregnant. Analysis Two mixed logit models were estimated, one to assess the statistical significance between scenarios and one in maximum acceptable risk space to allow comparison of the magnitudes of parameters between scenarios. Sample Women with RRMS who were considering having a child in the future, recruited from a UK MS patient register. Results Sixty respondents completed the survey. Participants preferred no treatment in 12.6% of choices in the ‘now’ scenario, rising significantly to 37.6% in the ‘trying to conceive’ scenario and 60.3% in the ‘pregnant’ scenario (Kruskal–Wallis p < 0.001). This pattern corresponds with results from models that included a no-treatment alternative-specific constant (ASC) capturing differences between taking and not taking a DMT not specified by the attributes. The ASC was lower in the trying to conceive scenario than in the now scenario, and lower still in the pregnant scenario, indicating an intrinsic preference for no treatment. Participants also placed relatively less preference on reducing relapses and avoiding disease progression in the trying to conceive and pregnant scenarios compared with a lower risk of problems with pregnancy. In the trying to conceive scenario, participants’ preference for treatments with shorter washout periods increased. Conclusion Women with RRMS considering having a child prefer DMTs with more favourable reproduction-related attributes, even when not trying to conceive. Reproductive issues also influenced preferences for DMT attributes not directly related to pregnancy, with preferences dependent on the life circumstances in which choices were made. The design of the DCE highlights the benefits of considering the scenario in which participants make choices, as they may change over time

Latent variables in discrete choice experiments

This paper describes and applies a general approach for incorporating factors with structural equations into models for discrete choice. The approach gives form to the covariance matrix in random coefficient models. The factors act directly on the random coefficients as unobserved attributes. The structural equations allow the factors to act on each other building structures that can represent a variety of concepts such as global heterogeneity and segmentation. The practical outcomes include parsimonious and identified models with rich covariances and better fit. Of greater interest is the ability to specify models that represent and test theory on the relationships between the taste heterogeneities for covariates and in particular between the attributes within a discrete choice experiment. The paper describes the general model and then applies it to a discrete choice experiment with seven attributes. Four competing specifications are evaluated, which demonstrates the ability of the model to be identified and parsimonious. The four specifications also demonstrate how competing a priori knowledge of the structure of the attributes used in the experiment can be empirically tested and evaluated. The outcomes include new behavioral insights and knowledge about choice and choice processes for the subject area of discrete choice experiments