Variation in Treatment Priorities for Chronic Hepatitis C: A Latent Class Analysis.
Academic Article
Overview
abstract
BACKGROUND: Data describing patients' priorities, or main concerns, are essential to inform important decisions in healthcare, including treatment planning, diagnostic testing, and the development of programs to improve access and delivery of care. To date, the majority of studies performed does not account for variability in patients' priorities, and as a consequence may not effectively inform end users. The objective of this study was to examine the value of segmentation analysis as a method to illustrate variability in priorities for treatment of chronic hepatitis C (HCV). METHODS: We elicited patients' main concerns when considering antiviral therapy for HCV using a Best-Worst Scaling experiment (Case 1) with ten objects. Latent class analysis was used to estimate part-worth utilities and the probability that each respondent belongs to each segment. RESULTS: In the aggregate, subjects (N = 162) had three main concerns: (1) not being cured; (2) experiencing a lot of side effects; and (3) developing viral resistance to therapy. Segmentation into two groups demonstrated that both groups prioritized the likelihood of cure and coping with side effects, but that only one group (n = 78) was concerned about developing viral resistance to therapy, while subjects in the second group (n = 84) prioritized being able to keep up with their responsibilities. Further segmentation revealed distinct clusters of patients with unique priorities. CONCLUSIONS: Patients' priorities vary significantly. Preference studies should consider including methods to determine whether distinct clusters of priorities and/or concerns exist in order to accurately inform end users' decision making.
