Patient Preferences for Unresectable Hepatocellular Carcinoma Treatments: A Discrete-Choice Experiment.
Daneng LiRuoding TanSairy HernandezNorelle ReillyCooper BussbergCarol MansfieldPublished in: Cancers (2023)
Treatments for unresectable hepatocellular carcinoma (HCC) have varying benefit-risk profiles. We elicited 200 US patients' preferences for attributes associated with various first-line systemic treatments for unresectable HCC in a discrete-choice experiment (DCE) survey. Respondents answered nine DCE questions, each offering a choice between two hypothetical treatment profiles defined by six attributes with varying levels: overall survival (OS), months of maintained daily function, severity of palmar-plantar syndrome, severity of hypertension, risk of digestive-tract bleeding, and mode and frequency of administration. A random-parameters logit model was used to analyze the preference data. Patients regarded an additional 10 months of maintaining daily function without decline to be as important or more important than 10 additional months of OS, on average. Respondents valued avoiding moderate-to-severe palmar-plantar syndrome and hypertension more than extended OS. A respondent would require >10 additional months of OS (the greatest increase presented in the study) on average to offset the increased burden of adverse events. Patients with unresectable HCC prioritize avoiding adverse events that would severely impact their quality of life over mode and frequency of administration or digestive-tract bleeding risk. For some patients with unresectable HCC, maintaining daily functioning is as important or more important than the survival benefit of a treatment.
Keyphrases
- end stage renal disease
- locally advanced
- ejection fraction
- liver metastases
- blood pressure
- chronic kidney disease
- newly diagnosed
- physical activity
- case report
- peritoneal dialysis
- atrial fibrillation
- decision making
- machine learning
- radiation therapy
- patient reported outcomes
- magnetic resonance
- computed tomography
- deep learning
- arterial hypertension