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Table 3 Results of the random effects models

From: Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia

Factors Coefficient Confidence interval σ 0C Confidence interval σ 0S Confidence interval σ 0M(S) Confidence interval
LL UL LL UL LL UL LL UL
n 0.0005 -0.0005 0.0009 0.0244 0.0165 0.0404 0.0489 0.0289 0.0759 0.000 0.000 0.0039
Δ -0.1169 -0.2041 -0.0285 0.0245 0.0163 0.0402 0.0359 0.0159 0.0405 0.000 0.000 0.0039
ρ 0.1489 0.0295 0.2636 0.0245 0.0165 0.0405 0.0369 0.0022 0.0579 0.000 0.000 0.0039
  1. Each factor was evaluated individually in the random effects linear regression model. The coefficients were inverse transformed to the original scale of the difference of classification model accuracy between MA- and individual classification approach
  2. Abbreviations: LL lower limit, UL upper limit
  3. Symbols: n: the number of samples in each generated dataset; Δ: the log2 fold change of differentially expressed (DE) genes. ρ: pairwise correlation of DE genes. σ 0C , σ 0S and σ 0M(S) are the standard deviation of the random intercepts with respect to classification model, scenario in the simulation study and the number of studies used for selecting relevant features via meta-analysis approach. See Method section for more details regarding the random effect models