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In some cases, the variable definitions are quite consistent across the sample of effect sizes, which leads to an increasing probability level (larger than 5%).
Performing a meta-analysis of the same sample of effect estimates allows us to go beyond the descriptive analysis in two ways.
We present here a sample of effect size metrics relevant to χ, t, or F tests.
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The effect sizes in the studies that were carried out, indeed, are assumed to represent a random sample of these effect sizes; hence the term random effects is used since there is an array of true effects.
Sampling of effects is described in more detail by Kizilkaya et al. [ 20].
Allelic values were assigned to the QTL by independent sampling of effects from the Laplace distribution.
Additive allelic values were assigned to each QTL by independent sampling of effects from a Laplace distribution.
Statistical analysis is used to extrapolate a sample of load effect values from the simulation period to the required design period.
In practice it is computationally less demanding to form the same offset by adding to the current residuals the current sample of the effect that will be updated.
Note that the sampling variation of effect size has been incorporated since the convolution already takes into account the random Gaussian noise added to the actual z-value.
This paper addresses the important issue of out-of-sample estimation of effect sizes and generalisation when assessing correlated changes in structure and function in longitudinal studies.
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