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One way to choose an effect size for power analyses is by relying on pilot data.
First, the length of the vector (sample size) does not generally change the measured fractal-like value for power analyses (left panel) with slopes < 1.0 and is not a factor for Whittle estimates for data with slopes approximately < 1.5.
For power analyses, we used the statistical software PASS (www.ncss.com).
There is a need for power analyses that address a range of possible relationships.
This effect size provided a realistic estimate of the expected reduction in fall risk and was used as the basis for power analyses.
Results of studies exploring the effectiveness of cognitive treatment programs in socially phobic patients [ 27, 28] available at the time of the study were used for power analyses.
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The program Quanto V was used for genetic power analyses for continuous outcomes (e.g. number of cigarettes smoked).
Third, the standard deviations of the simulated results are relatively smaller for the Whittle estimates than for the power analyses.
For the M D P P model, the prior on the concentration parameter, χ∼ Gamma, was set to Gamma(2,2) for the validation analyses and Gamma(1.5,18.1) for the power analyses.
We therefore specify distinct benchmarks for the power analyses: we seek to have good power for (a) OR = 3 and a variant with a frequency of 10%; (b) OR = 2 for a common variant (>20%).
Monte Carlo simulations provide an alternative and a validity check for software power analyses.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com