Single-patient (n-of-1) trials: A pragmatic clinical decision methodology for patient-centered comparative effectiveness research
Open accessAbstract
OBJECTIVE: To raise awareness among clinicians and epidemiologists that single-patient (n-of-1) trials are potentially useful for informing personalized treatment decisions for patients with chronic conditions. STUDY DESIGN AND SETTING: We reviewed the clinical and statistical literature on methods and applications of single-patient trials and then critically evaluated the needs for further methodological developments. RESULTS: Existing literature reports application of 2,154 single-patient trials in 108 studies for diverse clinical conditions; various recent commentaries advocate for wider application of such trials in clinical decision making. Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials have the potential for widespread acceptance by patients and clinicians as an effective modality for increasing the therapeutic precision. Bayesian and adaptive statistical methods hold promise for increasing the informational yield of single-patient trials while reducing participant burden, but are not widely used. Personalized applications of single-patient trials can be enhanced through further development and application of methodologies on adaptive trial design, stopping rules, network meta-analysis, washout methods, and methods for communicating trial findings to patients and clinicians. CONCLUSIONS: Single-patient trials may be poised to emerge as an important part of the methodological armamentarium for comparative effectiveness research and patient-centered outcomes research. By permitting direct estimation of individual treatment effects, they can facilitate finely graded individualized care, enhance therapeutic precision, improve patient outcomes, and reduce costs.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Such investigations (especially of risk) are typically not feasible via nomothetic group-based designs.
“Such investigations (especially of risk) are typically not feasible via nomothetic group-based designs [55].”
From Brief and Telehealth Acceptance and Commitment Therapy (ACT) Interventions for Stress in Inflammatory Bowel Disease (IBD): A Series of Single Case Experimental Design (SCED) Studies (2022) · cites this paper as [55]
The Introduction (section 1; page unavailable) says parallel-group trials cannot feasibly address all clinically important questions and that average treatment effects may obscure individual differences in risks and benefits. But the Bayesian methods section (section 4; page unavailable) says some studies can use either a parallel-group design or single-patient trials. The paper does not establish that investigations of individual risk are typically infeasible with group-based designs. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSCEDs provide evidence of the effectiveness of interventions, drawing on substantially fewer participants than nomothetic approaches such as RCTs.
“In doing this, SCEDs provide evidence of the effectiveness of interventions, drawing on substantially fewer participants than nomothetic approaches (such as RCTs) [51,52,53,54,55,56,57,58].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 44 · Cited by 239
- Authors
- Duan, N., Kravitz, R. L., & Schmid, C. H.
- Affiliations
- Columbia University (US)
- University of California, Davis (US)
- Brown University (US)
- Corresponding author
- Duan, Naihua
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 66
- Issue
- 8
- Pages
- S21-S28
- ISSN
- 0895-4356 (ISSN-L)
- PMC
- PMC3972259
- OpenAlex
- W1974781686
- Cited by
- 239
- References
- 44
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 12 · 2023: 11 · 2022: 36 · 2021: 20 · 2020: 17 · 2019: 23 · 2018: 20 · 2017: 18 · 2016: 13 · 2015: 23 · 2014: 12 · 2013: 2
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Clinical Epidemiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (P30 MH090322, R01 MH079387, R01 CA140419, 1 R01 NR013938, UL1TR000002)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- single-patient trials, N-of-1 trials, personalized medicine, individual treatment effects, clinical decision-making, chronic disease, Bayesian analysis, adaptive trial design, stopping rules, network meta-analysis, patient-centered outcomes research, comparative effectiveness research
- MeSH terms
- Bayes Theorem, Chronic Disease, Decision Support Techniques, Humans, Patient Participation, Pilot Projects, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care, Patient Selection, Cross-Over Studies, Evidence-Based Medicine, Comparative Effectiveness Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review