Treatment Preferences among Patients with Hormone-Sensitive Prostate Cancer in France, Spain, China, South Korea, and Japan: A Discrete-Choice Experiment
Bibliographic record
Abstract
Background. Treatment preferences of patients with high-risk localized prostate cancer (HRLPC) and metastatic hormone-sensitive prostate cancer (mHSPC) are expected to be influenced by cultural and institutional differences across countries. We aimed to evaluate this expectation by quantifying the importance of treatment outcomes for patients with HRLPC and mHSPC. Methods. A discrete-choice experiment survey included adults (≥18 y of age) diagnosed with HRLPC or mHSPC from 5 countries—France, Spain, China, South Korea, and Japan—with or without previous experience of androgen-deprivation therapy. A latent-class random-parameters logit model was used to evaluate the importance patients assigned to treatment attributes and the consistency in treatment preferences across the 5 countries. Results. In total, 468 patients completed the survey. Respondents with shared treatment preferences from all 5 countries had a chance of being in pooled class 1 (44.5%) or pooled class 2 (37.9%). The main factors of concern were skin rash, fatigue, and use of steroids for pooled class 1 and chemotherapy-associated problems for pooled class 2. Our analysis could not explain class assignments based on clinically relevant characteristics of patients, which were used as covariates. Conclusion. Despite cultural and institutional differences across the 5 countries, our model identified significant consistency in treatment preferences among patients with prostate cancer. Given the attribute levels in our study, efficacy was the most significant driver of patient preference. We also found that using 2 sets of preferences was sufficient to reasonably characterize the perspectives of about 80% of surveyed patients. That these 2 patient classes differed in terms of treatment concerns but not in clinical factors highlights the need for promoting communication between patients and clinicians about treatment choices. Highlights Our study demonstrates that discrete-choice experiments (DCEs) are valuable for capturing health-related preferences among patients with prostate cancer. Contextual factors, such as efficacy and the country-specific health care system in which choices are presented, influence the ability to pool DCE data across countries. DCEs have the potential to enhance patient-centered care, shaping the future of evidence-based health care decision making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".