random.icm {NMA}

Jackson's random inconsistency model

Description

Jackson's random inconsistency model for modelling the design-by-treatment interactions. Model-based testing results for heterogeneity and inconsistency (design-by-treatment interactions) and the I2-statistics are presented.

Usage

random.icm(x)

Arguments

x

Output object of setup

Value

Results of the analysis of Jackson's random inconsistency model and I2-statistics are presented.

• 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.

• number of designs: Number of designs.

• designs: Study designs (combinations of treatments of individual trials) on the network.

• Coef. (vs. treat 1): Regression coefficients estimates and their SEs, 95% confidence intervals and P-values.

• Between-studies_SD: Between-studies SD estimate.

• Between-designs_SD: Between-designs SD estimate.

• Likelihood ratio tests for the variance components: Results of the likelihood ratio tests for comparing (1) the fixed- and random-effects models without inconsistency effects (heterogeneity), (2) the random-effects models with and without inconsistency effects (inconsistency), and (3) the fixed-effect model without inconsistency effects and the random-effects model with inconsistency effects (heterogeneity + inconsistency).

• Heterogeneity and inconsistency statistics: R-statistics and I2-statistics for comparing (1) the fixed- and random-effects models without inconsistency effects (heterogeneity), (2) the random-effects models with and without inconsistency effects (inconsistency), and (3) the fixed-effect model without inconsistency effects and the random-effects model with inconsistency effects (heterogeneity + inconsistency).

References

Jackson, D., White, I. R., and Riley, R. D. (2012). Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Statistics in Medicine 31: 3805-3820.

Jackson, D., Barrett, J. K., Rice, S., White, I. R., and Higgins, J. P. T. (2014). A design-by-treatment interaction model for network meta-analysis with random inconsistency effects. Statistics in Medicine 33, 3639-3654.

Law, M., Jackson, D., Turner, R., Rhodes, K., and Viechtbauer, W. (2016). Two new methods to fit models for network meta-analysis with random inconsistency effects. BMC Medical Research Methodology 16, 87.

Nikolakopoulou, A., White, I. R., and Salanti, G. (2021). Network meta-analysis. In: Schmid, C. H., Stijnen, T., White, I. R., eds. Handbook of Meta-Analysis. CRC Press; pp. 187-217.

Examples

data(heartfailure)

hf2 <- setup(study=study,trt=trt,d=d,n=n,measure="OR",ref="Placebo",data=heartfailure)

random.icm(hf2)