The demands of traditional experimental methods are often seen as barriers to clinical inquiry because of their rigorous structure, requiring control groups and large numbers of homogenous subjects, typically unavailable in clinical settings. The experimental model also deals with averages and generalizations across groups of individuals, which may not allow the researcher to differentiate characteristics of those patients who responded favorably to treatment from those who did not improve. Therefore, although group generalizations are important for explaining behavioral phenomena, clinicians understand that group performance is relevant only if it can contribute to making decisions about individual patients.
Single-subject designs (SSDs) involve serial observations of individuals before, during, and after interventions, providing feedback that reflects clinical outcomes. As a patient-level investigation, this pragmatic approach can facilitate evidence-based practice (EBP) by providing clinicians with practical information to improve decision-making within a clinical environment and to assess the impact of programs of care.1–4 The purpose of this chapter is to describe a variety of SSDs and to explore issues related to their structure, application, analysis, and interpretation of treatment effectiveness.
Consider the following clinical research scenario:
Several years ago, a study was done to determine if the occurrence of stuttering would be different if adults read aloud at “usual” or “fast-as-possible” rates.5 A group of 20 adults was tested in a repeated measures design and no significant difference was seen between the two conditions based on a comparison of means.
Okay, so reading rate had no effect on stuttering—or did it? It turns out that a closer look at individual results showed that 8 subjects actually decreased their frequency of stuttering, 1 did not change, and 11 demonstrated an increase during the faster speaking condition. By drawing conclusions from average treatment effects, an important differentiation among individual performances was obscured, potentially affecting clinical outcomes.
Limitations of Randomized Trials
Because the application of research findings is often viewed through the lens of the randomized controlled trial (RCT), such studies generally incorporate large samples, randomization, and controlled protocols. Findings are assumed to be generalizable—but the assumption that one treatment’s effect can be generalized to all patients is rarely true.2 (If it were, we would not need so many different medications for arthritis, depression, diabetes, or heart disease—as evidenced by TV commercials!)
Because group studies typically take measurements at only two or three points in time, they may miss variations in response that occur over time. And sometimes a particular patient might benefit from a treatment that is shown to be inferior in a randomized trial. Therefore, randomized group studies can be ambiguous for purposes of clinical decision-making for an individual patient.
In the spirit of EBP, there is an increasing recognition among ...