Identifying Distinct Patient Preference Patterns in Second-Line Treatment for Type 2 Diabetes: A Latent Class Analysis of a Discrete Choice Experiment
Ann-Kathrin Fischer, Andrew Sadler, Elke Mathey, Axel C. MühlbacherBackground:
Treatment decisions in type 2 diabetes mellitus (T2D) require knowledge of patient preferences. The German National Health Care Guideline emphasizes preference-based decision making, with treatment goals negotiated in light of individual risk profiles, therapy-related effects, and life circumstances. Systematic preference heterogeneity suggests these contextual factors shape how patients prioritize attributes, posing a central challenge for medical decision making.
Methods:
Latent class analysis (LCA) was applied to data from a discrete choice experiment (DCE) conducted between August and November 2023 among 583 individuals with T2D in Germany. Participants completed 12 choice tasks using a partial profile design. Treatment options were described by risks of myocardial infarction, stroke, neuropathy, nausea, and severe hypoglycemia as well as weight change, intake mode, and schedule. Analyses were conducted for the overall sample and stratified by treatment experience (population 1: monotherapy,
Results:
In a 3-class solution (
Limitations:
LCA findings should be interpreted as hypothesis-generating representations of distinct decision logics. Furthermore, limitations inherent to the DCE design, including attribute reduction and partial profile approach, as well as potential variance in response behavior, were carefully considered and addressed.
Conclusions:
Patients with T2D exhibit distinct preference structures rather than uniform tradeoffs, differing fundamentally in whether disease-related, treatment-related, or administration attributes dominate their evaluations.
Implications:
Identifying latent preference patterns may enhance the transparency of tradeoffs in participatory treatment deliberation and support structured, guideline-aligned medical decision making.