ArticleThe patient2016
Variation in Treatment Priorities for Chronic Hepatitis C: A Latent Class Analysis.
Article in The patient, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review.PharmacoEconomics · 2018Pooled it
- Using Best-Worst Scaling to Investigate Preferences in Health Care.PharmacoEconomics · 2016Pooled it
- Time-dependent, patient-centered perceptions of quality measures for total joint arthroplasty: a cross-sectional, choice modeling study.BMC musculoskeletal disorders · 2025Article
- Engaging patients and caregivers in prioritizing symptoms impacting quality of life for Duchenne and Becker muscular dystrophy.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2018Article
- Preference phenotypes to facilitate shared decision-making in rheumatoid arthritis.Annals of the rheumatic diseases · 2018Article
- Big Data's Role in Precision Public Health.Frontiers in public health · 2018Review
- What's Important to the Patient? Informational Needs of Patients Making Decisions About Hepatitis C Treatment.The patient · 2017Article
- A Framework for Instrument Development of a Choice Experiment: An Application to Type 2 Diabetes.The patient · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundData 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).
methodsWe 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.
resultsIn 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.
conclusionsPatients' 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.