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Her children noticed she commonly lost her train of thought in mid-sentence, and often asked them if they had carried out the tasks that she mistakenly thought she had asked them to do. She was considering quitting her job, which involved analyzing data and writing reports, she got disoriented driving, and she mixed up the names of her pets. Visual inspection of the data suggests an effective treatment in the top panel but an ineffective treatment in the bottom panel.
IX. Chapter 9: Factorial Designs
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).
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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.
Multiple-Baseline Design Across Settings
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.
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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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.
Multiple baseline
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.
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Dr. Werthman discusses vasectomy reversal and the second chances it gives couples who want to build a biological family after vasectomy. In this design two or more treatments are alternated relatively quickly on a regular schedule. Alzheimer’s affects approximately 5.4 million Americans and 30 million people globally.
2: Single-Subject Research Designs
In the bottom panel of Figure 10.6, however, the changes in level are fairly small. And although there appears to be an increasing trend in the treatment condition, it looks as though it might be a continuation of a trend that had already begun during baseline. This pattern of results strongly suggests that the treatment was not responsible for any changes in the dependent variable—at least not to the extent that single-subject researchers typically hope to see. Another important aspect of single-subject research is that the change from one condition to the next does not usually occur after a fixed amount of time or number of observations. Specifically, the researcher waits until the participant’s behavior in one condition becomes fairly consistent from observation to observation before changing conditions. The idea is that when the dependent variable has reached a steady state, then any change across conditions will be relatively easy to detect.
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.
Recall that we encountered this same principle when discussing experimental research more generally. The effect of an independent variable is easier to detect when the “noise” in the data is minimized. SCEDs can identify the optimal treatment for an individual person rather than the average person in a group (Dallery & Raiff, 2014; Davidson et al., 2021; Hekler et al., 2020). That is, a person’s outcomes in one phase are compared to outcomes in another phase. In a typical study, replications are achieved within and/or across several individuals; this allows for strong inferences about causation between behavior and the treatment (or levels thereof).
It can be especially telling when a trend changes directions—for example, when an unwanted behaviour is increasing during baseline but then begins to decrease with the introduction of the treatment. While reversal designs can be used to compare effects of interventions, multiple baseline designs provide experimental control for testing one intervention but do not compare different interventions. These comparisons can be made for several different interventions with the combination of both designs to demonstrate experimental control and compare effects of the interventions. They were interested in how a school-wide bullying prevention program affected the bullying behaviour of particular problem students.
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.
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).
In this design, multiple baselines are either established for one participant or one baseline is established for many participants. The percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. A baseline phase is followed by separate phases in which different treatments are introduced. The dependent variable ranges between 12 and 16 units during the baseline, but drops down to 10 units with treatment and mostly decreases until the end of the study, ranging between 4 and 10 units. Just like with many home designs, the inverted floor plan isn't everyone's cup of tea. From a practical perspective, the idea of having to lug groceries up another flight of steps might not be palatable without the help of the aforementioned elevator.
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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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