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For the median, the effect coefficients of 3′UTR, 5′UTR, MIR, L2, MaLR, ELN and RTE are significant (p < 0.05), where the effect (coefficient) bars are longer than the corresponding error pikes.
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The analysis is based on the median-effect principle and the median effect equation by Chou (1974): fa/ fu=(D/ D m) m, where D is the dose of the drug; D m is the median-effect dose; fa is the fraction affected by the dose, fu the fraction unaffected by the dose, fu=1− fa; and m is an exponent signifying the sigmoidy (shape) of the dose effect plot.
The difference in the median effect size between the BA and Pla groups for each study is plotted in relation to the total dose of β-alanine supplemented in Fig. 3.
We expect the median to soften the effect of outliers.
These observed differences reflect the median effect over the 7-year duration of the study period where PVI was under constant evolution with different iterations of new devices and techniques, but LEB, as a procedure, was well established.
Note that the linear correlation coefficient, r, describes the conformity of the data to the median effect principle.
Means from this analysis (derived from β-coefficients) estimated the median effect in the population after they had been back-transformed.
To determine the nature of combination of the two drugs, the median effect principle outlined by Chou [ 55] was utilized.
This software calculates the median effect dose, Dm (analogous to the IC50), of the drug combinations using the median effect equation.
The median effect is defined as the average between the mean control and the mean effect produced by the highest tested concentration of E2.
The level of QTL complexity we chose matches that observed in the HS mice and is conservative for this analysis because the median effect size in the HS is only 3% (Valdar et al., 2006).
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