ArticleHarvard data science review2022
N-of-1 Trials, Their Reporting Guidelines, and the Advancement of Open Science Principles.
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 2 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
2 citing papers in PubMed.
- The Family of Single-Case Experimental Designs.Harvard data science review · 2022Article
- 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
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
N-of-1 trials are multiple crossover trials done over time within a single person; they can also be done with a series of individuals. Their focus on the individual as the unit of analysis maintains statistical power while accommodating greater differences between patients than most standard clinical trials. This makes them particularly useful in rare diseases, while also being applicable across many health conditions and populations. Best practices recommend the use of reporting guidelines to publish research in a standardized and transparent fashion. N-of-1 trials have the SPIRIT extension for N-of-1 protocols (SPENT) and the CONSORT extension for N-of-1 trials (CENT). Open science is a recent movement focused on making scientific knowledge fully available to anyone, increasing collaboration, and sharing of scientific efforts. Open science goals increase research transparency, rigor, and reproducibility, and reduce research waste. Many organizations and articles focus on specific aspects of open science, for example, open access publishing. Throughout the trajectory of research (idea, development, running a trial, analysis, publication, dissemination, knowledge translation/reflection), many open science ideals are addressed by the individual-focused nature of N-of-1 trials, including issues such as patient perspectives in research development, personalization, and publications, enhanced equity from the broader inclusion criteria possible, and easier remote trials options. However, N-of-1 trials also help us understand areas of caution, such as monitoring of post hoc analyses and the nuances of confidentiality for rare diseases in open data sharing. The N-of-1 reporting guidelines encourage rigor and transparency of N-of-1 considerations for key aspects of the research trajectory.
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.