As the results of the test suggested possible existence of significant publication bias (p=0.01 in Egger's test), the final effect size was determined by applying trim and fill analysis in the random-effects model.
The present study provides a method to enhance the nonlinear dynamic reliability analysis in selecting the random variables and offers a way to design structure and machine in future work.
† P-values estimated by bootstrap analysis in the 1,000 random samples.
False Positive Report Probability †OR and CIs estimated by bootstrap analysis in the 1,000 random samples.
The overall difference in concentrations of alanine aminotransferase was significant in the fixed effects meta-analysis, but not in the random effects analysis (–1.0 U/L, 95% confidence interval –2.5 to 0.6).
A physically consistent general scaling law is obtained by rigorous mathematical analysis in the framework of random vibration theory and the rules of safe-life fatigue analysis.
In the analysis using the random effects method, we found that D allele and DD genotype were not associated with SRNS risk (D: OR = 1.60, P = 0.26; DD: OR = 1.90, P = 0.38; Figure 1-A for D, Figure 2-A for DD; Table 4).
In the analysis using the random effects method, we found D allele and DD genotype were not associated with SSNS risk (D: OR = 1.24, P = 0.28; DD: OR = 1.72, P = 0.15; Figure 1-B for D, Figure 2-B for DD; Table 5).
To build the model, we first included in a classic Cox survival model only the hospitalization level and group level variables which were statistically significant at the 10% level in KM analysis; thereafter the random effects were added.
Where relative risks (and their 95%% confidence intervals) were not generated in Tunçalp [ 14] due to heterogeneity, these were generated for the analysis using the random effects model in StatsDirect (Version 2.7.8, 2010), so that incremental outcomes and costs could be calculated.
