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Dr. Werthman performs these procedures at an outpatient surgery center located in Beverly Hills and limits his surgery schedule to only one procedure a day, allowing him to focus solely on his patient and their needs. Second thoughts after a vasectomy happen for many reasons, and each family has their own story. Thanks to Dr. Werthman’s experience and in-depth expertise, most of his vasectomy reversal patients are new fathers within a year of their vasectomy reversal surgery. For more than 15 years, Los Angeles fertility specialist Dr. Werthman has performed 3-4 vasectomy reversals each week, with an overall 98% success rate. On average, that's 150 babies born each year, to families who thought they may never have children again.
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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.
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Multiple-Baseline Design Across Settings
Another example of a situation where an integrated approach may be useful is selecting a diet for weight control. Many diets for weight control that vary in their macronutrient intake—such as low carb, higher fat versus low fat, and higher carb—have their proponents and favorable biological mechanisms. However, direct comparisons of these diets basically show that they achieve similar weight control with large variability in outcome. Thus, while the average person on a low-fat diet does about the same as the average person on a low-carb diet, some people on the low-carb diet do very well, while some fail. Some of the people who fail on the low-fat diet would undoubtedly do well on the low-carb diet, and some who fail on the low-fat diet would do well on the low-carb diet. However, there are drawbacks to this design, such as irreversibility of behavior.
Single-Subject Research Designs
A family is suing Texas Children's Hospital for an "unintended vasectomy" which resulted from a routine hernia surgery. This flipped layout, sometimes called a reverse-story home, might sound unusual if you're the type of person who likes a traditional home layout. And it could certainly be out of place for a primary residence in a suburban neighborhood, where there's not much to see other than the neighbors' homes. But many a blueprint for a vacation home features an inverted layout to better take in views of the great outdoors. Founded in 1993, The Motley Fool is a financial services company dedicated to making the world smarter, happier, and richer.
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The results of single-subject research can also be analyzed using statistical procedures—and this is becoming more common. There are many different approaches, and single-subject researchers continue to debate which are the most useful. The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the t test or analysis of variance are applied (Fisch, 2001). (Note that averaging across participants is less common.) Another approach is to compute the percentage of nonoverlapping data (PND) for each participant (Scruggs & Mastropieri, 2001). This is the percentage of responses in the treatment condition that are more extreme than the most extreme response in a relevant control condition. In the study of Hall and his colleagues, for example, all measures of Robbie’s study time in the first treatment condition were greater than the highest measure in the first baseline, for a PND of 100%.
One solution to these problems is to use a multiple-baseline design, which is represented in Figure 10.5 “Results of a Generic Multiple-Baseline Study”. Personalized medicine suggests that diets should be individualized to achieve the best results. SCEDs would be one way to show ‘proof of concept’ that a particular diet is better than a standard healthy diet. First, people would be randomized to experimental (including SCEDs) or control (not basing diet on SCEDs).
The greater the percentage of nonoverlapping data, the stronger the treatment effect. Still, formal statistical approaches to data analysis in single-subject research are generally considered a supplement to visual inspection, not a replacement for it. The mean and standard deviation of each participant’s responses under each condition are computed and compared, and inferential statistical tests such as the t test or analysis of variance are applied (Fisch, 2001)[3]. (Note that averaging across participants is less common.) Another approach is to compute the percentage of non-overlapping data (PND) for each participant (Scruggs & Mastropieri, 2001)[4].
D-5: Use single-subject experimental designs (e.g., Reversal, Multiple Baseline, Multielement, Changing Criterion) ©
(The researchers used handheld computers to help record the data.) After 2 weeks, they implemented the program at one school. They found that the number of aggressive behaviors exhibited by each student dropped shortly after the program was implemented at his or her school. But with their multiple-baseline design, this kind of coincidence would have to happen three separate times—a very unlikely occurrence—to explain their results. As an example, consider a study by Scott Ross and Robert Horner (Ross & Horner, 2009). The emphasis of a research program may be on translating basic science findings to clinical interventions.
It can include additional treatment conditions and returns to baseline., also called the ABA designThe simplest reversal design, in which there is a baseline condition (A), followed by a treatment condition (B), followed by a return to baseline (A).. During the first phase, A, a baselineA condition in a single-subject research design in which the dependent variable is measured repeatedly in the absence of any treatment. Most designs begin with a baseline condition, and many return to the baseline condition at least once. This is the level of responding before any treatment is introduced, and therefore the baseline phase is a kind of control condition.
In the top panel of Figure 10.6, there are fairly obvious changes in the level and trend of the dependent variable from condition to condition. Furthermore, the latencies of these changes are short; the change happens immediately. This pattern of results strongly suggests that the treatment was responsible for the changes in the dependent variable.
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For example, if a treatment seemed to reduce the incidence of self-injury in a child with an intellectual delay, it would be unethical to remove that treatment just to show that the incidence of self-injury increases. The second problem is that the dependent variable may not return to baseline when the treatment is removed. For example, when positive attention for studying is removed, a student might continue to study at an increased rate. This could mean that the positive attention had a lasting effect on the student’s studying, which of course would be good. But it could also mean that the positive attention was not really the cause of the increased studying in the first place.
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