| global.ict | R Documentation |
Higgins' global inconsistency test based on the design-by-treatment interaction model. REML-based Wald test for the all possible design-by-treatment interactions on the network is performed.
global.ict(x)
x |
Output object of |
The results of the global inconsistency test are prrovided.
coding: A table that presents the correspondence between the numerical code and treatment categories (the reference category is coded as 1).
reference: Reference treatment category.
number of studies: Number of studies.
designs: Study designs (combinations of treatments of individual trials) on the network.
Coefficients of the design-by-treatment interaction model: Regression coefficients estimates and their SEs, 95% confidence intervals and P-values.
Between-studies_SD: Between-studies SD estimate.
Between-studies_COR: Between-studies correlation coefficient estimate (=0.50).
X2-statistic: Chi-squared statistic of the global inconsistency test.
df: Degree of freedom.
P-value: P-value of the global inconsistency test.
Higgins, J. P., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E., and White, I. R. (2012). Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research Synthesis Methods 3, 98-110.
Jackson, D., Boddington, P., and White, I. R. (2016). The design-by-treatment interaction model: a unifying framework for modelling loop inconsistency in network meta-analysis. Research Synthesis Methods 7, 329-332.
White, I. R., Barrett, J. K., Jackson, D., and Higgins, J. P. (2012). Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Research Synthesis Methods 3, 111-125.
data(heartfailure)
hf2 <- setup(study=study,trt=trt,d=d,n=n,measure="OR",ref="Placebo",data=heartfailure)
global.ict(hf2)
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