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Requiring an amyloid-β1-42 biomarker may improve the efficiency of a study, and simulations may help in planning studies

DOI: 10.1186/alzrt69

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The simulation study reported by Schneider and colleagues is a valuable contribution to the field of Alzheimer's disease. The study was conducted under a detailed protocol and clearly lays out the assumptions that were made and the criteria that were used for each set of simulations [1]. The article makes the point that some situations are complicated enough that standard power calculations do not capture the whole picture, because they require simplifying assumptions that may not hold. In these cases, power calculations may more accurately reflect reality when based on simulations that do not rely as much on distributional assumptions.In this study, one critical assumption is the basis of the main conclusion. Table 1 presents the results taken from Schneider and colleagues' study, including the mean and standard deviation (SD) for each group for the Alzheimer Disease Assessment Scale - cognitive subscale (ADAS-cog).The difference between group means divided by the SD is called a standardised difference (or Cohen's D value [2]) and allows estimation of power based on a t test. If you take the placebo group mean and subtract the treatment group mean and then divide that difference by the placebo SD, using numbers that are all shown in the table, you obtain the effect size shown in the third column, within the rounding error (25%, 35% and 45%). This exercise illustrates that the effect size used in this simulation study increases and decreases proportionally to the standardised difference, which is tied mathematically to the power. In other words, although the sensitivity of the ADAS-cog to decline over time is increased with the biomarker selection methods, the treatment difference was decreased in order to maintain the same standardised difference.Although 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

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