nma function

Network meta-analysis based on contrast-based approach using the multivariate meta-analysis model

Network meta-analysis based on contrast-based approach using the multivariate meta-analysis model

Network meta-analysis based on contrast-based approach using the multivariate random-effects meta-analysis model. The synthesis results and prediction intervals based on the consistency assumption are provided. The ordinary REML method and its improved higher order asymptotic methods (Noma-Hamura methods) are available.

nma(x, eform=FALSE, method="NH")

Arguments

  • x: Output object of setup
  • eform: A logical value that specify whether the outcome should be transformed by exponential function (default: FALSE)
  • method: Estimation and prediction method. NH: Noma-Hamura's improved REML-based methods (default). REML: The ordinary REML method. fixed: Fixed-effect model.

Returns

Results of the network meta-analysis using the multivariate meta-analysis model.

  • 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: The number of synthesized studies.
  • method: The estimation and prediction methods.
  • Coef. (vs. treat1): Estimates, their SEs, Wald-type 95% confidence intervals, and P-values for the grand mean parameter vector.
  • tau (Between-studies_SD) estimate: Between-studies SD (tau) estimate.
  • tau2 (Between-studies_variance) estimate: Between-studies variance (tau^2) estimate.
  • Multivariate H2-statistic: Jackson's multivariate H2-statistic.
  • Multivariate I2-statistic: Jackson's multivariate I2-statistic.
  • Test for Heterogeneity: Multivariate Q-statistic and P-value of the test for heterogeneity.
  • 95%PI: 95% prediction intervals.

References

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

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.

Noma, H., Hamura, Y., Gosho, M., and Furukawa, T. A. (2023). Kenward-Roger-type corrections for inference methods of network meta-analysis and meta-regression. Research Synthesis Methods 14 , 731-741.

Noma, H., Hamura, Y., Sugasawa, S., and Furukawa, T. A. (2023). Improved methods to construct prediction intervals for network meta-analysis. Research Synthesis Methods 14 , 794-806.

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.

Examples

data(heartfailure)

hf2 <- setup(study=study,trt=trt,d=d,n=n,measure="OR",ref="Placebo",data=heartfailure)
hf3 <- setup(study=study,trt=trt,d=d,n=n,measure="RR",ref="Placebo",data=heartfailure)
hf4 <- setup(study=study,trt=trt,d=d,n=n,measure="RD",ref="Placebo",data=heartfailure)

nma(hf2, eform=TRUE)
nma(hf3, eform=TRUE)
nma(hf4)
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