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However, it is subject to many possible competing hypotheses, making strong conclusions difficult. Variants on the AB design introduce ways to control for the competing hypotheses to allow for stronger conclusions. An alternative design would be to have everyone experience SCEDs for the first 3 months and then be randomized to either the optimal treatment identified during the first 3 months or an intervention randomly chosen among the interventions to be studied. This design has the advantage of randomization being after 3 months of study so that dropouts and nonadherers within the first 3 months would not be randomized in an intent-to-treat format. Whether the data is higher or lower based on a visual inspection of the data; a change in the level implies the treatment introduced had an effect. A baseline is established for several participants and the treatment is then introduced to each participant at a different time.
Extreme Makeover
This is an issue for older homeowners, particularly those who are looking to age in place. Homes with master bedrooms on the first level have been growing in popularity for that reason. While Dr. Werthman is internationally-renowned for his work in vasectomy reversal and a sought-after lecturer at urologic and fertility conferences, he is most passionate about treating patients at his practice in Los Angeles. He believes medicine and surgery should be practiced the “old-fashioned way", where each patient is given personal, individualized attention.
Integrating Multiple Baseline and Reversal Designs
In the bottom panel of Figure 10.5, however, the changes in level are fairly small. In addition to its focus on individual participants, single-subject research differs from group research in the way the data are typically analyzed. As we have seen throughout the book, group research involves combining data across participants. Group data are described using statistics such as means, standard deviations, Pearson’s r, and so on to detect general patterns. Finally, inferential statistics are used to help decide whether the result for the sample is likely to generalize to the population. This means plotting individual participants’ data as shown throughout this chapter, looking carefully at those data, and making judgments about whether and to what extent the independent variable had an effect on the dependent variable.
Multiple-Baseline Designs
Single-case experimental designs (SCEDs) represent a family of experimental designs to examine the relationship between one or more treatments or levels of treatment and changes in biological or behavioral outcomes. These designs originated in early experimental psychology research (Boring, 1929; Ebbinghaus, 1913; Pavlov, 1927), and were later expanded and formalized in the fields of basic and applied behavior analysis (Morgan & Morgan, 2001; Sidman, 1960). In the top panel of Figure 10.4, there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. In the bottom panel of Figure 10.4, however, the changes in level are fairly small. There are two potential problems with the reversal design—both of which have to do with the removal of the treatment.
It is possible that something else changed at around the same time and that this extraneous variable is responsible for the change in the dependent variable. But if the dependent variable changes with the introduction of the treatment and then changes back with the removal of the treatment, it is much clearer that the treatment (and removal of the treatment) is the cause. In other words, the reversal greatly increases the internal validity of the study. Group data are described using statistics such as means, standard deviations, correlation coefficients, and so on to detect general patterns. For example, if a treatment seemed to reduce the incidence of self-injury in a developmentally disabled child, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. Specifically, the researcher waits until the participant’s behaviour in one condition becomes fairly consistent from observation to observation before changing conditions.
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If the dependent variable is much higher or much lower in one condition than another, this suggests that the treatment had an effect. A second factor is trendOne factor that is considered in the visual inspection of single-subject data. An increase or decrease in the independent variable over several observations., which refers to gradual increases or decreases in the dependent variable across observations. If the dependent variable begins increasing or decreasing with a change in conditions, then again this suggests that the treatment had an effect.
Results from this test will indicate if a vasovasostomy or a vasoepididymostomy procedure is needed. 2 years ago, you performed a reversal on me, 15 yrs after the original vasectomy. After 3 months of effort, (hardly the right word for it, ha ha), success was achieved and I sit here with a beautiful daughter in my arms.
The goal may be to collect early phase translational research as a step toward a fully powered RCT—(Epstein et al., 2021). A common approach to early phase translational research is to implement a small, underpowered RCT to secure a ‘signal’ of a treatment effect and an effect size. This is a problematic approach to pilot research, and it is not advocated by the NIH as an approach to early phase translational research (National Center for Complementary and Integrative Health, 2020).
2: Single-Subject Research Designs
Of course, failure to receive an effective treatment is common in RCTs for people who are randomized to control conditions, but unlike control groups in RCTs, all participants eventually receive treatment. In yet a third version of the multiple-baseline design, multiple baselines are established for the same participant but in different settings. For example, a baseline might be established for the amount of time a child spends reading during his free time at school and during his free time at home. Then a treatment such as positive attention might be introduced first at school and later at home. Again, if the dependent variable changes after the treatment is introduced in each setting, then this gives the researcher confidence that the treatment is, in fact, responsible for the change.
If achieving stability due to uncontrolled sources of biological or environmental variation is not possible, a reversal design may not be appropriate to evaluate a treatment, though it may be useful to identify the sources of variability (Sidman, 1960). Finally, for a reversal to a baseline, a no-treatment phase may be inappropriate in investigating treatment effects for a very ill patient. SCEDs provide a framework to determine whether changes in a target behavior(s) or symptom are in fact a function of the intervention. The fundamentals of an SCED involve repeated measurement, replication of conditions (e.g., baseline and intervention conditions), and the analysis of effects with respect to each individual serving as his or her own control. This process can be useful for identifying the optimal treatment for an individual (Dallery & Raiff, 2014; Davidson et al., 2021), treating rare diseases (Abrahamyan et al., 2016), and implementing early phase translational research (Czajkowski et al., 2015).
Single-case experimental designs provide flexible, rigorous, and cost-effective approaches that can be used in personalized medicine to identify the optimal treatment for an individual patient. SCEDs represent a broad array of designs, and personalized (N-of-1) designs are a prominent example, particularly in medicine. These designs can be incorporated into RCTs, and they can be integrated using meta-analysis techniques. The main limitation to a multiple baseline design is that some people (or behaviors) may be kept in baseline or control conditions for extended periods before treatment is implemented.
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