ArticleHarvard data science review2022
Bayesian Models for N-of-1 Trials.
Article in Harvard data science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Digital N-of-1 trials and their application in experimental physiology.Experimental physiology · 2026Review
- Personalized Nutrition Recommendations Using a Bayesian Mixture Model of Concentration Constraints and Intake Preferences.Statistics in medicine · 2026Article
- N-of-1 trials in clinical research: Methodological foundations, statistical approaches and implementation challenges.British journal of clinical pharmacology · 2026Review
- 5α-Reductase Isoenzymes: From Neurosteroid Biosynthesis to Neuropsychiatric Outcomes.NeuroSci · 2026Review
- Individual-centric N-of-1 trials: a case study assessing the effect of alcohol abstinence on mood levels.BMC medical research methodology · 2026Article
- Instrumental variable approach to estimating individual causal effects in N-of-1 trials: application to ISTOP study.Biostatistics (Oxford, England) · 2025Article
- Predictors and process moderators of EMDR therapy for depressive symptoms: protocol for a series of observational N-of-1 trials.Frontiers in psychology · 2025Article
- A methodological review of randomised n-of-1 trials.Trials · 2024Review
- N-of-1 trials: The epitome of personalized medicine?Journal of clinical and translational science · 2023Article
- Introducing Data Sciences to N-of-1 Designs, Statistics, Use-Cases, the Future, and the Moniker 'N-of-1' Trial.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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
We describe Bayesian models for data from N-of-1 trials, reviewing both the basics of Bayesian inference and applications to data from single trials and collections of trials sharing the same research questions and data structures. Bayesian inference is natural for drawing inferences from N-of-1 trials because it can incorporate external and subjective information to supplement trial data as well as give straightforward interpretations of posterior probabilities as an individual's state of knowledge about their own condition after their trial. Bayesian models are also easily augmented to incorporate specific characteristics of N-of-1 data such as trend, carryover, and autocorrelation and offer flexibility of implementation. Combining data from multiple N-of-1 trials using Bayesian multilevel models leads naturally to inferences about population and subgroup parameters such as average treatment effects and treatment effect heterogeneity and to improved inferences about individual parameters. Data from a trial comparing different diets for treating children with inflammatory bowel disease are used to illustrate the models and inferences that may be drawn. The analysis shows that certain diets were better on average at reducing pain, but that benefits were restricted to a subset of patients and that withdrawal from the study was a good marker for lack of benefit.
Indexed as
Identifiers
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.