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Significantly skewed data were transformed using arcsin transformations.
Prior to latency regression analysis, latency data were transformed using inverse transformation due to non-normal distribution of residuals.
Raw data were transformed using log2 transformation and normalized using quantile normalization as previously suggested for Illumina miRNA microarray experiments [ 66].
All reported F- and p-values are from repeated measure analyses of variance (ANOVAs), all error bars depict standard errors, effects were judged significant when a.05 significance level was reached, and accuracy data were transformed using an arcsine transformation before performing ANOVAs.
The raw data were transformed using a logistic transformation (transformed utility=log ((1-utility)/utility)).
The data were transformed using the variance stabilization transformation (VST) [37] and normalized using the robust spline normalization (RSN) [36].
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In a next step, the data was transformed using either log2-transformation (log) or variance-stabilizing transformation (vst) [ 9].
To compare continuous variables we used t tests, with skewed data being transformed using a logarithmic transformation before analysis.
As such, the data was transformed using a log10 + 1 transformation, which yielded a normal distribution.
The data are transformed using modeling techniques and the resulting uncertainty is quantified through sensitivity analyses before making a recommendation regarding reimbursement.
All data was transformed using base e.
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