Single-Case Experimental Design in Clinical Interventions

Single-case experimental designs (SCEDs) are rigorous methodologies that evaluate the effect of clinical interventions on individual participants through systematic manipulation and repeated measurement. Typically involving alternating baseline and intervention phases within the same subject, these designs enable causal inference at the individual level and are especially valuable in personalised medicine, rare diseases and developmental disorders. Common variants include reversal (ABAB) designs, in which treatment is introduced and withdrawn, and multiple‐baseline designs, in which interventions commence at different time points across behaviours, settings or participants. Such designs offer high internal validity by demonstrating replication of effects and can address ethical constraints when large‐scale trials are impractical. Data analysis may combine structured visual inspection of phase graphs with quantitative indices of change, including non‐overlap metrics and time‐series modelling. Emerging applications span psychiatry, paediatric interventions and rehabilitation, where SCEDs inform adaptive treatment decisions and complement group trials. By capturing within‐person variability and facilitating rapid evaluation, single‐case approaches contribute to evidence‐based practice and the global shift towards tailored healthcare.

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Single-Case Experimental Design in Clinical Interventions publication trend

The graph below shows the total number of articles in single-case experimental design in clinical interventions across all publications each year (not limited to Nature Index journals).

Technical terms

Single-Case Experimental Design: An evaluative method using repeated measures and systematic intervention shifts within an individual to establish causal effects.

N-of-1 trial: A single-subject crossover study where a treatment is compared with a control or alternative condition in one participant.

Multiple-baseline design: A SCED variant introducing treatment at staggered times across behaviours, settings or individuals to demonstrate replicated effects.

Reversal design: Also known as ABAB design; involves withdrawal and reinstatement of an intervention to verify its impact.

Autocorrelation: The correlation of a variable with itself across successive time points, which can bias statistical inference if ignored.

Non-overlap effect size: A quantitative index that measures the degree to which data points in intervention phases differ from baseline observations.

References

1. Toward responsible clinical n-of-1 strategies for rare diseases. Drug Discovery Today (2023).
2. Evidence and reporting standards in N-of-1 medical studies: a systematic review. Translational Psychiatry (2023).
3. A Multilevel Meta-analysis of Single-Case Research on Interventions for Internalizing Disorders in Children and Adolescents. Clinical Child and Family Psychology Review (2023).
4. Threats to Internal Validity in Multiple-Baseline Design Variations. Perspectives on Behavior Science (2022).
5. Dynamic modelling of n-of-1 data: powerful and flexible data analytics applied to individualised studies. Health Psychology Review (2017).
6. Analyzing Two-Phase Single-Case Data with Non-overlap and Mean Difference Indices: Illustration, Software Tools, and Alternatives. Frontiers in Psychology (2016).