Once you get into the analysis phase, graphs are a helpful way of detecting RTM (for example, a scatter plot of post-pre values plotted against baseline). (Alternatively, using the mean of several baseline measurements for the pre-value will be closer to the true mean than a single measurement). In the design phase, another approach to minimizing RTM would be to take 2 sets of baseline measurements at 2 different times - one baseline measurement to select groups, and the other baseline measure as a covariate. (It’s important to note, however, that using a control group may not be a complete solution when extreme values are chosen). Without the appropriate control group for comparison, it would be difficult to conclude the decline was not just due to regression to the mean. However, the treatment group experiences a much greater improvement. In both groups, as you can see, the more extreme the initial diastolic blood pressure (DBP), the greater the decline. The following two tables show the baseline and follow-up values for the treatment and control groups: Subjects were classified into 3 groups based on their screening values and randomized to treatment or control. The best solution, say most authors, is an appropriately designated control group.Ī classic example is Reader, et al.’s trial in mild hypertensive patients (1980). Luckily, there are corrections you can make at the design phase and/or analysis phase to minimize the risk of RTM. Two-phase sampling designs where a subset of the first sample based on initial value is chosen for further study (e.g., when selecting the highest or lowest risk group for sub-analyses or follow-up).Pre-post intervention study designs that target “high risk” groups (e.g., individuals with high blood pressure).RTM is a particular concern in two situations: The problem is that RTM can make the predictable change in repeated measures look like meaningful change due to a treatment. This is due to random measurement error or, put another way, non-systematic fluctuations around the true mean. RTM is a statistical phenomenon that occurs when unusually large or unusually small measurement values are followed by values that are closer to the population mean. So just what is regression to the mean (RTM)? This phenomenon, known as regression to the mean, has been used to explain everything from patterns in hereditary stature (as Galton first did in 1886) to why movie sequels or sophomore albums so often flop. Have you ever heard that “2 tall parents will have shorter children”?
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