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Cited by 86Statistical Methods in Clinical TrialsPsychotherapy Techniques and ApplicationsMeta-analysis and systematic reviews

Personalizing behavioral interventions through single-patient (N-of-1) trials

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Davidson, Karina W., Peacock, James, Kronish, Ian M., Edmondson, Donald·2014·Social and Personality Psychology Compass
DOI 10.1111/spc3.12121PMC PMC4175746
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Abstract

Abstract Behavioral interventions are typically studied with the use of a conventional between‐subject randomized controlled trial (RCT) design . In this design, the effect of an intervention on one group of patients is compared with the effect of a control condition on another group of patients, such that a between‐subject change is tested. A between‐subject design has an underlying assumption that there is a homogenous treatment effect for a behavioral intervention, drug, or psychotherapy and that the way the intervention operates in the study will tend to operate in the same way in many other patients. We review some of the philosophical and practical problems with the use of this design when a clinician is attempting to decide on a course of behavioral treatment aimed at within‐subject change in patients who are likely to have heterogeneous or unique responses to behavioral treatment. We also review the biases inherent in our current clinical practice model, which does not use any empirical data collection or design for testing if a treatment is useful, and also in the conventional between‐subject personalized medicine RCT designs . We propose increased use of single‐patient (also known as N‐of‐1) trials that employ within‐subject designs, in cases where treatment response is heterogeneous – as is the case for most psychological and behavioral treatments. Limitations of such designs include that they can only be used when the treatment is potentially reversible, the patient can act as their own control, and the outcome can be measured repeatedly. Increased use of within‐subject trials may address in many more instances the more clinically relevant question of how a specific patient will respond to a specific treatment and could introduce a more harmonious scientific approach into the way we treat our patients. We have incorporated a case presentation that illustrates the complexities of applying evidence drawn from these different designs to selecting and evaluating treatments for the behavioral issues commonly faced by clinicians and patients.

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  • Subgroup analyses are encouraged to better elucidate differences in treatment response between individuals.

    “Although subgroup analyses are now encouraged in an effort to better elucidate differences in treatment response between individuals, it requires large cohorts of patients for sufficiently powered analyses [23].”

    From Graded exposure treatment for adolescents with chronic pain (GET Living): Protocol for a randomized controlled trial enhanced with single case experimental design (2019) · cites this paper as [23]

    The section “Conventional Between-Subject Personalized Medicine: Definitions and Assumptions” describes subgrouping patients to better characterize individual differences and guide treatment testing. It does not say subgroup analyses are encouraged or that they require large cohorts for sufficient statistical power; the paper instead emphasizes the limitations of subgroup-based approaches for individual patients. (LLM, conf=0.81)

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  • Subgroup analyses require large cohorts of patients for sufficiently powered analyses.

    “Although subgroup analyses are now encouraged in an effort to better elucidate differences in treatment response between individuals, it requires large cohorts of patients for sufficiently powered analyses [23].”

    From Graded exposure treatment for adolescents with chronic pain (GET Living): Protocol for a randomized controlled trial enhanced with single case experimental design (2019) · cites this paper as [23]

    The section “Conventional Between-Subject Personalized Medicine: Definitions and Assumptions” describes dividing patients into subgroups and randomizing within them, but does not discuss the cohort size needed for statistical power. Searches for subgroup power and sample-size requirements found no passage addressing that claim; the Conclusions’ reference to large conventional RCTs concerns drug approval, not powered subgroup analyses. Page numbers were unavailable in our copy. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Authors
Davidson, K. W., Peacock, J., Kronish, I. M., & Edmondson, D.
Affiliations
Columbia University Irving Medical Center (US)
Corresponding author
Davidson, Karina W.
ORCID
Venue
Social and Personality Psychology Compass
Publisher
Wiley
Published
2014-08
Volume
8
Issue
8
Pages
408-421
ISSN
1751-9004 (ISSN-L)
DOI
10.1111/spc3.12121
PMC
PMC4175746
OpenAlex
W1554241201
Cited by
86
References
34
Citations by year
2026: 3 · 2025: 1 · 2024: 1 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 50 · 2019: 8 · 2018: 5 · 2017: 4 · 2016: 1 · 2015: 2
FWCI
2.46
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 4 d)
Locations
  • Social and Personality Psychology Compass (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (K24 HL084034, P01 HL088117, R01 HL115941)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
single-patient trials, N-of-1 trials, within-subjects design, between-subjects design, treatment response heterogeneity, personalized medicine, behavioral interventions, treatment selection, clinical decision-making, randomized controlled trials

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