Data from: How to use discrete choice experiments to capture stakeholder preferences in social work research
Data files
May 27, 2024 version files 25.58 MB

03_table_s2.csv

04_design_wide_cost_dummy.csv

05_design_long_cost_continuous.csv

08_population_data.csv

09_population_parameters.csv

12_choice_data.csv

14_results_cl.csv

15_results_rpl.csv

17_sample_size_rpl_results.csv

23_wtp_results_cl.csv

24_wtp_results_rpl.csv

README.md
Abstract
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 willingnesstopay 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.
README: Data from: How to Use Discrete Choice Experiments to Capture Stakeholder Preferences in Social Work Research
This dataset supports the worked example in:
Ellis, A. R., CryerCoupet, Q. R., Weller, B. E., Howard, K., Raghunandan, R., & Thomas, K. C. (2024). How to use discrete choice experiments to capture stakeholder preferences in social work research. Journal of the Society for Social Work and Research. Advance online publication. https://doi.org/10.1086/731310
The referenced article introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. In a DCE, researchers ask participants to complete a series of choice tasks: hypothetical situations in which each participant is presented with alternative scenarios and selects one or more. For example, social work researchers may want to know how parents and other caregivers prioritize different aspects of mental health treatment when choosing services for their children. A DCE can explore this question by presenting scenarios that include different types of mental health care providers, treatment methods, costs, locations, and so on. Caregivers’ stated choices in these scenarios can provide a lot of information about their priorities.
In a DCE, the scenarios presented to participants are called alternatives. The characteristics of the alternatives are called attributes, and the attribute values presented to participants are called levels. Participants' choices are typically analyzed according to random utility theory, which holds that utility is a latent variable underlying people's observed choices. Utility has a systematic component derived from the attributes of alternatives and the individual characteristics of participants, as well as a random component.
Our 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 submission includes the scripts (and, in some cases, Excel files) that we used to create and analyze the worked example, as well as the associated data files and images.
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 outofpocket costs) identified by dosReis and colleagues (2007, 2015) in a previous DCE. We employed an orthogonal design (so named because all pairs of attribute levels appear with equal frequency, making the attributes independent of each other) with 18 choice tasks. Our design included 1 continuous attribute (cost) and 12 categorical attributes, which were dummycoded. The online supplement shows all of the attribute levels and which combinations of levels were presented in the simulated DCE.
Using the parameter estimates published by dosReis et al., with slight adaptations, we simulated utility values for a population (N=100,000), then selected a sample (n=500) for analysis. Relying on random utility theory, we used the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models to estimate utility parameters. The MNL model assumes that the systematic component of utility is distributed uniformly across individuals. The RPL model assumes that utility varies randomly across individuals according to a specified distribution.
In addition to estimating utility, 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 dataset includes both the simulated source data and the processed results. The online supplement of the referenced article describes the methods in greater detail.
The next section of this document describes how the files in this submission relate to each other, and the two sections following that describe (a) the structure and content of the data files and (b) the structure and content of the Excel files, which contain mixtures of data and formulas. The code included with this submission can be used to replicate the data. Before the R code is run, the working directory must be set (near the top of each script, where it says "set your working directory here").
File structure
The following table describes the files in the order in which they were created. It also describes how they relate to each other.
File  Description  Source Program or Input File(s)  Output File(s) 

00_README.md  this file  
01_design.r  R code to identify an appropriate experimental design  03_table_s2.csv, 04_design_wide_cost_dummy.csv, 05_design_long_cost_continuous.csv  
02_design.sas  SAS code to identify an alternate design  
03_table_s2.csv  experimental design (shown in Table S2 of the online supplement)  01_design.r  
04_design_wide_cost_dummy.csv  experimental design, dummycoded  01_design.r  
05_design_long_cost_continuous.csv  experimental design, adjusted to make cost continuous  01_design.r  
06_population_simulation_input.xlsx  parameters used for population simulation  adapted from dos Reis et al. (2017)  
07_simulate_population.r  R code to simulate the study population  06_population_simulation_input.xlsx  08_population_data.csv, 09_population_parameters.csv 
08_population_data.csv  simulated population data  07_simulate_population.r  
09_population_parameters.csv  true values of the population parameters  07_simulate_population.r  
10_sample_size_cl.r  R code to estimate required sample size for multinomial logit (conditional logit) model  05_design_long_cost_continuous.csv, 09_population_parameters.csv  
11_simulate_sample.r  R code to select sample and simulate choice data  05_design_long_cost_continuous.csv, 08_population_data.csv  12_choice_data.csv 
12_choice_data.csv  choice data from the study sample  11_simulate_sample.r  
13_analysis.r  R code to run multinomial logit (conditional logit) and random parameter logit models  12_choice_data.csv  14_results_cl.csv, 15_results_rpl.csv 
14_results_cl.csv  multinomial logit (conditional logit) results  13_analysis.r  
15_results_rpl.csv  random parameter logit results  13_analysis.r  
16_sample_size_rpl_simulation.r  simulation to estimate sample size requirements for random parameter logit model  05_design_long_cost_continuous.csv, 08_population_data.csv, 14_results_cl.csv  17_sample_size_rpl_results.csv 
17_sample_size_rpl_results.csv  sample size simulation results  16_sample_size_rpl_simulation.r  
18_sample_size_rpl_analysis.r  R code to analyze sample size simulation results for random parameter logit model  09_population_parameters.csv, 17_sample_size_rpl_results.csv  
19_attribute_importance_analysis.xlsx  Excel file to analyze attribute importance based on attribute level range and parameter estimates  14_results_cl.csv, 15_results_rpl.csv  
20_attribute_importance_plot.r  R code to plot attribute importance results  19_attribute_importance_analysis.xlsx  21_attribute_importance.png 
21_attribute_importance.png  plot of attribute importance results  20_attribute_importance_plot.r  
22_wtp_analysis.r  R code to estimate willingness to pay with bootstrapped estimates and confidence intervals  12_choice_data.csv, 14_results_cl.csv  23_wtp_results_cl.csv, 24_wtp_results_rpl.csv 
23_wtp_results_cl.csv  willingnesstopay estimates for multinomial logit (conditional logit) model  22_wtp_analysis.r  
24_wtp_results_rpl.csv  willingnesstopay estimates for random parameter logit model  22_wtp_analysis.r  
25_wtp_summary.r  R code to summarize willingnesstopay results  23_wtp_results_cl.csv, 24_wtp_results_rpl.csv  26_wtp_plot.png 
26_wtp_plot.png  willingnesstopay plot  25_wtp_summary.r  
27_predicted_uptake.xlsx  Excel file to predict uptake of specific alternatives  14_results_cl.csv 
Description of data file contents
This section describes the contents of each individual data file, in the order in which the files are listed above.
03_table_s2.csv
experimental design (shown in Table S2 of the online supplement)
Each row in the file represents one of the 18 choice tasks. Each choice task had two alternatives. In the original study by dosReis et al., each attribute (including cost) had 3 levels (shown in the online supplement of the paper referenced above). In this file, each attribute level is coded as 1, 2, or 3.
Column  Description 

meds1  level of the medication administration attribute for alternative 1 
therapy1  level of the therapy location attribute for alternative 1 
accommodation1  level of the school accommodation attribute for alternative 1 
cgtraining1  level of the caregiver behavior training attribute for alternative 1 
prvcomm1  level of the provider communication attribute for alternative 1 
prvspec1  level of the provider specialty attribute for alternative 1 
cost1  level of the monthly outofpocket costs attribute for alternative 1 
meds2  level of the medication administration attribute for alternative 2 
therapy2  level of the therapy location attribute for alternative 2 
accommodation2  level of the school accommodation attribute for alternative 2 
cgtraining2  level of the caregiver behavior training attribute for alternative 2 
prvcomm2  level of the provider communication attribute for alternative 2 
prvspec2  level of the provider specialty attribute for alternative 2 
cost2  level of the monthly outofpocket costs attribute for alternative 2 
04_design_wide_cost_dummy.csv
experimental design, dummycoded
Each row in the file represents one of the 18 choice tasks. Each choice task had two alternatives. In the original study by dosReis et al., each attribute (including cost) had 3 levels (shown in the online supplement of the paper referenced above). In this file, each attribute level is coded using two dummy variables for levels 2 and 3, with level 1 as the reference category.
Column  Description 

meds1_2  =1 if medication administration = 2 for alternative 1, 0 otherwise 
meds1_3  =1 if medication administration = 3 for alternative 1, 0 otherwise 
therapy1_2  =1 if therapy location = 2 for alternative 1, 0 otherwise 
therapy1_3  =1 if therapy location = 3 for alternative 1, 0 otherwise 
accommodation1_2  =1 if school accommodation = 2 for alternative 1, 0 otherwise 
accommodation1_3  =1 if school accommodation = 3 for alternative 1, 0 otherwise 
cgtraining1_2  =1 if caregiver behavior training = 2 for alternative 1, 0 otherwise 
cgtraining1_3  =1 if caregiver behavior training = 3 for alternative 1, 0 otherwise 
prvcomm1_2  =1 if provider communication = 2 for alternative 1, 0 otherwise 
prvcomm1_3  =1 if provider communication = 3 for alternative 1, 0 otherwise 
prvspec1_2  =1 if provider specialty = 2 for alternative 1, 0 otherwise 
prvspec1_3  =1 if provider specialty = 3 for alternative 1, 0 otherwise 
cost1_2  =1 if monthly outofpocket costs = 2 for alternative 1, 0 otherwise 
cost1_3  =1 if monthly outofpocket costs = 3 for alternative 1, 0 otherwise 
meds2_2  =1 if medication administration = 2 for alternative 2, 0 otherwise 
meds2_3  =1 if medication administration = 3 for alternative 2, 0 otherwise 
therapy2_2  =1 if therapy location = 2 for alternative 2, 0 otherwise 
therapy2_3  =1 if therapy location = 3 for alternative 2, 0 otherwise 
accommodation2_2  =1 if school accommodation = 2 for alternative 2, 0 otherwise 
accommodation2_3  =1 if school accommodation = 3 for alternative 2, 0 otherwise 
cgtraining2_2  =1 if caregiver behavior training = 2 for alternative 2, 0 otherwise 
cgtraining2_3  =1 if caregiver behavior training = 3 for alternative 2, 0 otherwise 
prvcomm2_2  =1 if provider communication = 2 for alternative 2, 0 otherwise 
prvcomm2_3  =1 if provider communication = 3 for alternative 2, 0 otherwise 
prvspec2_2  =1 if provider specialty = 2 for alternative 2, 0 otherwise 
prvspec2_3  =1 if provider specialty = 3 for alternative 2, 0 otherwise 
cost2_2  =1 if monthly outofpocket costs = 2 for alternative 2, 0 otherwise 
cost2_3  =1 if monthly outofpocket costs = 3 for alternative 2, 0 otherwise 
05_design_long_cost_continuous.csv
experimental design, adjusted to make cost continuous
Each pair of rows in the file represents one of the 18 choice tasks, with one row for each alternative presented in the task. Each choice task had two alternatives. In the original study by dosReis et al., each attribute (including cost) had 3 levels (shown in the online supplement of the paper referenced above). In this file, the cost variable is coded as continuous. Otherwise, each attribute level is coded using two dummy variables for levels 2 and 3, with level 1 as the reference category.
Column  Description 

task  index of the choice task (1 to 18) 
alt  index of the alternative within the choice task (1 or 2) 
meds_2  =1 if medication administration = 2, 0 otherwise 
meds_3  =1 if medication administration = 3, 0 otherwise 
therapy_2  =1 if therapy location = 2, 0 otherwise 
therapy_3  =1 if therapy location = 3, 0 otherwise 
accommodation_2  =1 if school accommodation = 2, 0 otherwise 
accommodation_3  =1 if school accommodation = 3, 0 otherwise 
cgtraining_2  =1 if caregiver behavior training = 2, 0 otherwise 
cgtraining_3  =1 if caregiver behavior training = 3, 0 otherwise 
prvcomm_2  =1 if provider communication = 2, 0 otherwise 
prvcomm_3  =1 if provider communication = 3, 0 otherwise 
prvspec_2  =1 if provider specialty = 2, 0 otherwise 
prvspec_3  =1 if provider specialty = 3, 0 otherwise 
cost  monthly outofpocket costs in hundreds of US dollars 
08_population_data.csv
simulated population data
This file contains the population data that we simulated. Each row represents an individual. There are two columns for each categorical attribute and one column for cost. The number in each cell is the individual's utility for the specified attribute level, or, in the case of cost, for a $100 increase in cost.
Column  Description 

id  ID of the individual (1 to 100,000) 
meds_2  utility for medication administration = 2 
meds_3  utility for medication administration = 3 
therapy_2  utility for therapy location = 2 
therapy_3  utility for therapy location = 3 
accommodation_2  utility for school accommodation = 2 
accommodation_3  utility for school accommodation = 3 
cgtraining_2  utility for caregiver behavior training = 2 
cgtraining_3  utility for caregiver behavior training = 3 
prvcomm_2  utility for provider communication = 2 
prvcomm_3  utility for provider communication = 3 
prvspec_2  utility for provider specialty = 2 
prvspec_3  utility for provider specialty = 3 
cost  utility for a $100 increase in monthly outofpocket costs 
09_population_parameters.csv
true values of the population parameters
This file contains the true values of the population parameters, obtained by calculating the mean and standard deviation of each of the 13 utility variables in the simulated population data. There are 13 rows for the means, followed by 13 rows for the standard deviations.
Column  Description 

parameter  name of the parameter 
truth  true calculated value of the parameter 
12_choice_data.csv
choice data from the study sample
We selected a simple random sample of 500 individuals from the simulated population. We used each individual's simulated utility values to determine what choices the individual would make given the 18 choice tasks. For each individual and each choice task (500 x 18 = 9,000 rows), this file shows the attribute levels for the two alternatives in the choice task, as well as the choice that the individual "made."
Column  Description 

id  study ID of the individual (1 to 500) 
case  unique row ID (1 to 9,000) 
task  index of the choice task (1 to 18) 
meds_2.1  =1 if medication administration = 2 for alternative 1, 0 otherwise 
meds_3.1  =1 if medication administration = 3 for alternative 1, 0 otherwise 
therapy_2.1  =1 if therapy location = 2 for alternative 1, 0 otherwise 
therapy_3.1  =1 if therapy location = 3 for alternative 1, 0 otherwise 
accommodation_2.1  =1 if school accommodation = 2 for alternative 1, 0 otherwise 
accommodation_3.1  =1 if school accommodation = 3 for alternative 1, 0 otherwise 
cgtraining_2.1  =1 if caregiver behavior training = 2 for alternative 1, 0 otherwise 
cgtraining_3.1  =1 if caregiver behavior training = 3 for alternative 1, 0 otherwise 
prvcomm_2.1  =1 if provider communication = 2 for alternative 1, 0 otherwise 
prvcomm_3.1  =1 if provider communication = 3 for alternative 1, 0 otherwise 
prvspec_2.1  =1 if provider specialty = 2 for alternative 1, 0 otherwise 
prvspec_3.1  =1 if provider specialty = 3 for alternative 1, 0 otherwise 
cost.1  monthly outofpocket costs in hundreds of US dollars for alternative 1 
meds_2.2  =1 if medication administration = 2 for alternative 2, 0 otherwise 
meds_3.2  =1 if medication administration = 3 for alternative 2, 0 otherwise 
therapy_2.2  =1 if therapy location = 2 for alternative 2, 0 otherwise 
therapy_3.2  =1 if therapy location = 3 for alternative 2, 0 otherwise 
accommodation_2.2  =1 if school accommodation = 2 for alternative 2, 0 otherwise 
accommodation_3.2  =1 if school accommodation = 3 for alternative 2, 0 otherwise 
cgtraining_2.2  =1 if caregiver behavior training = 2 for alternative 2, 0 otherwise 
cgtraining_3.2  =1 if caregiver behavior training = 3 for alternative 2, 0 otherwise 
prvcomm_2.2  =1 if provider communication = 2 for alternative 2, 0 otherwise 
prvcomm_3.2  =1 if provider communication = 3 for alternative 2, 0 otherwise 
prvspec_2.2  =1 if provider specialty = 2 for alternative 2, 0 otherwise 
prvspec_3.2  =1 if provider specialty = 3 for alternative 2, 0 otherwise 
cost.2  monthly outofpocket costs in hundreds of US dollars for alternative 1 
choice  the number of the alternative that the individual chose (1 or 2) 
14_results_cl.csv
multinomial logit (conditional logit) results
The multinomial logit (MNL) model estimates only the mean utility because it assumes constant utility across individuals with random error. This file contains the estimated mean utilities from the MNL model.
Column  Description 

parameter  name of the parameter 
estimate  estimated mean 
stderr  standard error of the estimated mean 
15_results_rpl.csv
random parameter logit results
The random parameter logit (RPL) model estimates mean utilities and deviations around the means. In this case, the deviations are standard deviations because we simulated, and assumed in the RPL model, a normal distribution for each utility parameter. This file contains the estimated mean utilities and estimated standard deviations from the RPL model.
Column  Description 

parameter  name of the parameter 
estimate  estimated mean or standard deviation 
stderr  standard error of the estimate 
17_sample_size_rpl_results.csv
sample size simulation results
We ran simulations to estimate the sample size required to estimate the parameters in the RPL model with 80% statistical power and an alpha level of .05. This file contains the simulation results.
Column  Description 

n  sample size (250 to 2,500) 
sim  simulation number (1 to 50 for each sample size) 
parameter  name of the parameter 
estimate  estimated mean or standard deviation 
stderr  standard error of the estimate 
23_wtp_results_cl.csv
willingnesstopay estimates for multinomial logit (conditional logit) model
This file contains estimates of participants' willingness to pay (WTP) for differences in attributes (for example, how much they would be willing to pay for their child to see one type of provider versus another). The estimates in this file came from the MNL model. We used 200 bootstrap replications to estimate 95% confidence intervals for the WTP estimates. Each row in the file represents one of the 200 bootstrap replications. Each column represents an attribute level (2 or 3). Each data cell contains a WTP estimate for a given attribute level, relative to the reference level (1) for that attribute.
Column  Description 

meds_2  WTP for medication administration = 2 
meds_3  WTP for medication administration = 3 
therapy_2  WTP for therapy location = 2 
therapy_3  WTP for therapy location = 3 
accommodation_2  WTP for school accommodation = 2 
accommodation_3  WTP for school accommodation = 3 
cgtraining_2  WTP for caregiver behavior training = 2 
cgtraining_3  WTP for caregiver behavior training = 3 
prvcomm_2  WTP for provider communication = 2 
prvcomm_3  WTP for provider communication = 3 
prvspec_2  WTP for provider specialty = 2 
prvspec_3  WTP for provider specialty = 3 
24_wtp_results_rpl.csv
willingnesstopay estimates for random parameter logit model
This file contains estimates of participants' willingness to pay (WTP) for differences in attributes (for example, how much they would be willing to pay for their child to see one type of provider versus another). The estimates in this file came from the RPL model. We used 200 bootstrap replications to estimate 95% confidence intervals for the WTP estimates. Each row in the file represents one of the 200 bootstrap replications. Each column represents an attribute level (2 or 3). Each data cell contains a WTP estimate for a given attribute level, relative to the reference level (1) for that attribute.
Column  Description 

meds_2  WTP for medication administration = 2 
meds_3  WTP for medication administration = 3 
therapy_2  WTP for therapy location = 2 
therapy_3  WTP for therapy location = 3 
accommodation_2  WTP for school accommodation = 2 
accommodation_3  WTP for school accommodation = 3 
cgtraining_2  WTP for caregiver behavior training = 2 
cgtraining_3  WTP for caregiver behavior training = 3 
prvcomm_2  WTP for provider communication = 2 
prvcomm_3  WTP for provider communication = 3 
prvspec_2  WTP for provider specialty = 2 
prvspec_3  WTP for provider specialty = 3 
Description of Excel file contents
This section describes the contents of each individual Excel file, in the order in which the files are listed above.
These files include formulas, so they are considered software and hosted on Zenodo.
06_population_simulation_input.xlsx
parameters used for population simulation
This file contains 3 rows for each of the 7 attributes. Each set of 3 rows corresponds to the 3 levels used for an attribute in the original study by dosReis et al. (and shown in the online supplement of our article). The attribute names and descriptions follow:
 meds = medication administration
 therapy = therapy location
 accomodation = school accommodation
 cgtraining = caregiver behavior training
 prvcomm = provider communication
 prvspec = provider specialty
 cost = monthly outofpocket cost
The levels of each attribute are numbered 1 through 3.
Column  Description 

attribute (dos Reis et al., 2017)  name and level of the attribute 
estimate (dosReis et al. 2017)  estimated utility of the attribute as reported by dosReis et al. 
SE (dosReis et al. 2017)  standard error of the estimated utility as reported by dosReis et al. 
means, converted to dummy coding  estimated mean utility for levels 2 and 3 of each categorical parameter (relative to level 1) and for the cost parameter 
SD, using abs(mean)/2  standard deviation of utility, set at half the absolute value of the mean 
For each attribute, to estimate the mean utility for levels 2 and 3, we subtracted the level 1 parameter estimate from the level 2 and 3 parameter estimates, respectively.
To estimate the mean utility for each $100 increase in cost in the same way, we calculated the difference in utility between levels 1 and 2 ($50 and $150, respectively), calculated a third of the difference in utility between levels 2 and 3 ($150 and $450, respectively), and took the average.
We used the means and standard deviations in this file to simulate the population data from which we selected our sample.
19_attribute_importance_analysis.xlsx
Excel file to analyze attribute importance based on attribute level range and parameter estimates
We used this file to assess the relative importance of each attribute. We measured importance using the utility range of each attribute. The utility range is the difference between the highest and lowest estimated utilities associated with levels of the attribute. Because the cost attribute had the largest utility range, we expressed the importance of the other attributes relative to the importance of the cost attribute.
This file contains two tabs, one labeled CL for the conditional logit (multinomial logit) model, and the other labeled RPL for the random parameter logit model.
Column  Description 

parameter  name of the parameter 
estimate  estimated mean from the MNL (CL) or RPL model 
attribute  name of the attribute associated with the parameter 
min  minimum level of the attribute 
max  maximum level of the attribute 
rng  placeholder for calculating maxmin 
U(min)  estimated utility of the attribute's minimum level 
U(max)  estimated utility of the attribute's maximum level 
range  absolute difference between U(min) and U(max) 
range/max  importance of the attribute relative to the most important attribute, expressed as a percentage 
27_predicted_uptake.xlsx
Excel file to predict uptake of specific alternatives
We designed three treatment packages (Alternatives A, B, and C) with different sets of attributes (described in the online supplement). We then used the MNL results to predict the uptake of each package, that is, what percentage of the population would choose each package, given (a) a choice among the three or (b) a choice between the package and the status quo. We used this file to make the predictions. We also estimated how much the cost of each package would have to change in order for that package to have the same uptake as each other package.
The top portion of the file contains the data used to predict uptake:
Column  Description 

parameter  name of the parameter (attribute) 
CL estimate  utility estimate from the conditional logit (multinomial logit) model 
Alternative A  The level of the attribute under Alternative A, dummy coded 
Alternative B  The level of the attribute under Alternative B, dummy coded 
Alternative C  The level of the attribute under Alternative C, dummy coded 
The bottom portion of the file contains the uptake predictions:
Label in Column A  Label in Column B  Value in Column Labeled Alternative A, B, or C 

Uptake  utility  estimated utility of the alternative 
Uptake  exp(utility)  exponentiated utility 
Uptake  pred. prob. of selecting (versus others)  predicted probability of selecting the alternative, versus the others 
Uptake  pred. prob. of selecting (versus status quo)  predicted probability of selecting the alternative, versus the status quo 
Estimated change in cost to balance uptake  utility, except for cost  utility of the alternative minus the utility for cost 
Estimated change in cost to balance uptake  cost that would make uptake equal to that of Alternative A  cost that would make the alternative's uptake the same as that of Alternative A 
Estimated change in cost to balance uptake  cost that would make uptake equal to that of Alternative B  cost that would make the alternative's uptake the same as that of Alternative B 
Estimated change in cost to balance uptake  cost that would make uptake equal to that of Alternative C  cost that would make the alternative's uptake the same as that of Alternative C 
Source of datagenerating parameters
We adapted the datagenerating parameters from the following sources:
 dosReis, S., Mychailyszyn, M. P., Myers, M., & Riley, A. W. (2007). Coming to terms with ADHD: How urban AfricanAmerican families come to seek care for their children. Psychiatric Services, 58(5), 636641. https://doi.org/10.1176/ps.2007.58.5.636
 dosReis, S., Ng, X., Frosch, E., Reeves, G., Cunningham, C., & Bridges, J. (2015). Using bestworst scaling to measure caregiver preferences for managing their child’s ADHD: A pilot study. The Patient, 8(5), 423–431. https://doi.org/10.1007/s4027101400984
Code/Software
Most of the code in this submission was implemented in R version 4.3.2. The single SAS program (which we used to identify an alternate experimental design) was implemented in SAS Version 9.4. For some analyses, we used Microsoft Excel Version 16.84.
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 outofpocket costs) identified by dosReis and colleagues in a previous DCE. We employed an orthogonal design with 1 continuous variable (cost) and 12 dummycoded 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.