ArticleJournal of clinical epidemiology2018
Patient preferences for personalized (N-of-1) trials: a conjoint analysis.
Article in Journal of clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review.PharmacoEconomics · 2025Pooled it
- Current Practices for Accounting for Preference Heterogeneity in Health-Related Discrete Choice Experiments: A Systematic Review.PharmacoEconomics · 2022Pooled it
- Data mining techniques in psychotherapy: applications for studying therapeutic alliance.Scientific reports · 2023Article
- N-of-1 trials: The epitome of personalized medicine?Journal of clinical and translational science · 2023Article
- Lessons for Understanding Central Nervous System HIV Reservoirs from the Last Gift Program.Current HIV/AIDS reports · 2022Review
- Personalized (N-of-1) Trials for Patient-Centered Treatments of Multimorbidity.Harvard data science review · 2022Article
- Conduct and Implementation of Personalized Trials in Research and Practice.Harvard data science review · 2022Article
- Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care.Harvard data science review · 2022Article
- Clinical care and therapeutic trials in PLS.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2020Article
- Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey.BMJ open · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveDespite their promise for increasing treatment precision, Personalized Trials (i.e., N-of-1 trials) have not been widely adopted. We aimed to ascertain patient preferences for Personalized Trials. STUDY DESIGN AND
settingWe recruited 501 adults with ≥2 common chronic conditions from Harris Poll Online. We used Sawtooth Software to generate 45 plausible Personalized Trial designs comprising combinations of eight key attributes (treatment selection, treatment type, clinician involvement, blinding, time commitment, self-monitoring frequency, duration, and cost) at different levels. Conditional logistic regression was used to assess relative importance of different attributes using a random utility maximization model.
resultsOverall, participants preferred Personalized Trials with no costs vs. $100 cost (utility difference 1.52 [standard error 0.07], P < 0.001) and with less vs. more time commitment/day (0.16 [0.07], P < 0.015) but did not hold preferences for the other six attributes. In subgroup analyses, participants ≥65 years, white, and with income ≤$50,000 were more averse to costs than their counterparts (P all <0.05).
conclusionTo optimize dissemination, Personalized Trial designers should seek to minimize out-of-pocket costs and time burden of self-monitoring. They should also consider adaptive designs that can accommodate subgroup differences in design preferences.
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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.