Abstract
A recent article by Schneider and colleagues has generated a lot of interest in simulation studies as a way to improve study design. The study also illustrates the foremost principal in simulation studies, which is that the results of a simulation are an embodiment of the assumptions that went into it. This simulation study assumes that the effect size is proportional to the mean to standard deviation ratio of the Alzheimer Disease Assessment Scale - cognitive subscale in the population being studied. Under this assumption, selecting a subgroup for a clinical trial based on biomarkers will not affect the efficiency of the study, despite achieving the desired increase in the mean to standard deviation ratio.
Highlights
A recent article by Schneider and colleagues has generated a lot of interest in simulation studies as a way to improve study design
The difference between group means divided by the standard deviation (SD) is called a standardised difference and allows estimation of power based on a t test
This same type of approach seems to have been taken for the Clinical Dementia Rating scale sum of boxes (CDR-sb), calculating the observed effect size (Cohen’s D value) by taking the difference between group means divided by the SD does not correspond to the planned effect size shown in Schneider and colleagues’ Table 3 [1]
Summary
In Alzheimer’s disease, use of the method assumes that the treatment is expected to reduce the decline by the same percentage across different trial scenarios This assumption may be justified when studying the same patient population with different instruments; it may not be reasonable when comparing different disease stages, since a treatment may not have the same percentage benefit in these different patient populations. When selecting a population based on biomarkers in order to increase the decline rate seen over the study period, it is not clear whether the same percentage effect would be expected in this subgroup, or whether the percentage effect might go down due to the more rapid progression of this subgroup This method does have the advantage of not depending on the sensitivity of the instrument being used. This assumption results in very similar power between aMCI and biomarker selected groups
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