ArticleBMJ open2020
Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Analysis of N-of-1 trials using Bayesian distributed lag model with autocorrelated errors.Statistics in medicine · 2023Trial
- Article
- Clinical trials in rheumatology: patient motivations and concerns as key determinants of recruitment.Reumatologia · 2026Review
- Decision tool of medical endoscope maintenance service in Chinese hospitals: a conjoint analysis.BMC health services research · 2023Article
- Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine.Behavior research methods · 2023Article
- Article
- Recruitment and retention of clinical trial participants: understanding motivations of patients with chronic pain and other populations.Frontiers in pain research (Lausanne, Switzerland) · 2023Review
- How do study design features and participant characteristics influence willingness to participate in clinical trials? Results from a choice experiment.BMC medical research methodology · 2022Article
- The Application of Preference Elicitation Methods in Clinical Trial Design to Quantify Trade-Offs: A Scoping Review.The patient · 2022Article
- Evaluating Personalized (N-of-1) Trials in Rare Diseases: How Much Experimentation Is Enough?Harvard data science review · 2022Article
- Conduct and Implementation of Personalized Trials in Research and Practice.Harvard data science review · 2022Article
- An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial.Harvard data science review · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectiveTo describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences.
designEach participant was presented with 18 conjoint questions via an online survey. Each question provided two choices of Personalised Trials that were defined by up to eight attributes, including treatment types, clinician involvement, study logistics and trial burden on a patient.
settingOnline survey of adults with at least two common chronic conditions in the USA.
participantsA nationally representative sample of 501 individuals were recruited from the Chronic Illness Panel by Harris Poll Online. Participants were recruited from several sources, including emails, social media and telephone recruitment of the target population.
main outcome measuresThe choice of Personalised Trial design that the participant preferred with each conjoint question.
resultsThere was large variability in participants' preferences for the design of Personalised Trials. On average, they preferred certain attributes, such as a short time commitment and no cost. Notably, a population-level analysis correctly predicted 62% of the conjoint responses. An empirical Bayesian analysis of the conjoint data, which supported the estimation of individual-level preferences, improved the accuracy to 86%. Based on estimates of individual-level preferences, patients with chronic pain preferred a long study duration (p≤0.001). Asthma patients were less averse to participation burden in terms of data-collection frequency than patients with other conditions (p=0.002). Patients with hypertension were more cost-sensitive (p<0.001).
conclusionThese analyses provide a framework for elucidating individual-level preferences when implementing novel patient-centred interventions. The data showed that patient preference in Personalised Trials is highly variable, suggesting that individual differences must be accounted for when marketing Personalised Trials. These results have implications for advancing precise interventions in Personalised Trials by indicating when rigorous scientific principles, such as frequent monitoring, is feasible in a substantial subset of patients.
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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.