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Odds ratios, 95% CIs and p-values from the multiple logistic regression test variables included in the final model are reported.
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Statistical analysis was performed by Student t, Mann-Whitney, χ2, and multiple logistic regression tests.
Data were analyzed using descriptive statistics, t-test, Mann–Whitney U-test, chi-square test, One-way ANOVA, Kruskal-Wallis test, and binary multiple logistic regression tests.
The AUC for the most predictive single miRNA was compared to the AUC from the multiple logistic regression model using Delong's test for comparing nested AUCs.
Results from the multiple logistic regression analyses are shown in Table 3.
Odds ratios (ORs) were estimated by exponentiating the corresponding coefficients from the multiple logistic regression model.
Table 3 gives the results from the multiple logistic regression analysis, estimating the effect of multiconfounders.
Table 3 shows the adjusted odds ratios from the multiple logistic regression modelling.
Adjusted subgroup effects were calculated for the three specified subgroups from the multiple logistic regression model.
Hosmer DW, Lemeshow S. A goodness of fit test for the multiple logistic regression model.
The multiple logistic regression model produced from the analysis had an outcome classification accuracy of 88% when tested on an independent sample.
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