Do Simulated Patient Preferences Match a Published DCE? The Adam Replication
A health-services researcher or HEOR/medical affairs team deciding whether to trust a synthetic discrete choice experiment on patient treatment preferences needs a check against real patient behavior before that ranking informs a patient-experience or communications program. Adam et al. ran a published discrete choice experiment (DCE) on treatment-process attributes with patients in Berlin and Munich, comparing complementary and conventional medicine. An internal synthetic run on the same design produced a rank ordering that can be checked directly against the original human ordering. A ranking without its run details is marketing, so population size, run date, and method are not published here.
The decision this replication informs
Before committing budget to a full human fielding, a market-access or health-system team can run a synthetic version of the same choice design first. The question isn't whether the synthetic run is correct in isolation. It's whether its ranking of attributes agrees with how real patients in a published study actually chose.
What did the original human study measure?
Adam et al. fielded the DCE with patients in Berlin and Munich, asking them to trade off active listening, time with the provider, and cost when choosing between complementary and conventional medicine. The published result is a ranking of which attributes patients weighted more heavily.
"DCE results showed that the treatment process attributes 'active listening' and 'time' were most relevant to all patients."
Adam and colleagues, Patient (2018) (source)
What did the synthetic replication compare?
The misses matter as much as the hits, so a synthetic version of the same DCE design was run against a simulated population, with population size and run date not published here. The outcome of interest is the rank ordering agreement between the two orderings, not a re-derivation of the original study's individual respondent data.
| Attribute | Measured in human DCE | Measured in synthetic DCE |
|---|---|---|
| Active listening | Yes | Yes |
| Time with provider | Yes | Yes |
| Cost | Yes | Yes |
The synthetic ranking and the published human ranking agreed in direction. Naming what a comparison does not cover is what lets a buyer check it: it is not a claim that the synthetic run reproduces the original study's respondent-level data or extends automatically to a different patient population.
Where this fits into a validation workflow
A team doesn't have to choose between a synthetic study alone and a full human field study upfront. Subconscious can move a team from a simulated experiment to real-human testing or validation without changing the underlying causal question, using a synthetic DCE as a first-pass check against a published study like this one before deciding whether a new population needs its own fielded validation. Related synthetic-to-human comparisons are collected in case studies, and the underlying method is documented on the research page.
What does this replication not establish?
A published result is only useful to a buyer who can see its edges, so a rank correlation against one published study is not a live clinical validation. It does not certify accuracy for a specific new health-system decision, and it does not substitute for direct patient research on the buyer's own population. The comparison speaks to whether attribute ordering agreed in direction between the two studies. It does not speak to individual-level prediction, calibration for a different patient group, or performance across a new set of treatment-process attributes.