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Empirical standard error is defined as the average of standard errors of the estimated treatment effects across all simulation replications.
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The size of the points was proportional to the average of the inverse of standard errors of the two studies for that particular risk factor's estimate, so that larger points were those for which there was more precision.
The average of 100 estimates of standard error based on the cross-validation samples is 749 904.
We adopted the average of these standard errors (0.29) as the reproducibility of the CI values.
We adopted the average of these standard errors (2.2) as the reproducibility of the ESR signal intensity data obtained in this study.
Filtering on expression level with threshold of standard error average× 4 were used for GSE6472.
Observed correlation, EXP Expected correlation, ESTIM DISCR Estimate of the local discrimination, RMSE Root-mean-square average of the standard errors, RS-MC Raw Score to Measure Correlation.
For each model, we calculated the bias, simulation standard deviation (SD), average of estimated standard error (SE) and mean squared error (MSE) of the point estimator of treatment effect (i.e. β1), empirical coverage of the 95% confidence interval around β1 and the empirical statistical power.
Scores on the CPQ11 14 – ISF: 16 ranged from 0 to 43, with an average of 10.23 (standard error = 0.32).
Average and standard errors of the mean (SEM) were calculated from at least three independent determinations.
Marginal (empirical) reliability was estimated by calculating the ratio of the average of the squared standard errors of observed expected a-posteriori (EAP) scores over the observed EAP score variance, and subtracting that ratio from one.
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