How to use discrete choice experiments to capture stakeholder preferences in social work research

The primary article (cited below under "Related works") introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. The article includes an online supplement with a worked example demonstrating DCE design and analysis with realistic simulated data. The worked example focuses on caregivers' priorities in choosing treatment for children with attention deficit hyperactivity disorder. This dataset includes the scripts (and, in some cases, Excel files) that we used to identify appropriate experimental designs, simulate population and sample data, estimate sample size requirements for the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models, estimate parameters using the MNL and RPL models, and analyze attribute importance, willingness to pay, and predicted uptake. It also includes the associated data files (experimental designs, data generation parameters, simulated population data and parameters, simulated choice data, MNL and RPL results, RPL sample size simulation results, and willingness-to-pay results) and images. The data could easily be analyzed using other software, and the code could easily be adapted to analyze other data. Because this dataset contains only simulated data, we are not aware of any legal or ethical considerations.

Methods
In the worked example, we used simulated data to examine caregiver preferences for 7 treatment attributes (medication administration, therapy location, school accommodation, caregiver behavior training, provider communication, provider specialty, and monthly out-of-pocket costs) identified by dosReis and colleagues in a previous DCE. We employed an orthogonal design with 1 continuous variable (cost) and 12 dummy-coded variables (representing the levels of the remaining attributes, which were categorical). Using the parameter estimates published by dosReis et al., with slight adaptations, we simulated utility values for a population of 100,000 people, then selected a sample of 500 for analysis. Relying on random utility theory, we used the mlogit package in R to estimate the MNL and RPL models, using 5,000 Halton draws for simulated maximum likelihood estimation of the RPL model. In addition to estimating the utility parameters, we measured the relative importance of each attribute, estimated caregivers’ willingness to pay (WTP) for differences in attributes (e.g., how much they would be willing to pay for their child to see one type of provider versus another) with bootstrapped 95% confidence intervals, and predicted the uptake of three treatment packages with different sets of attributes. This submission includes both the simulated source data and the processed results. The online supplement of the primary article describes the methods in greater detail.