Preprint / Version 1

Within-study covariance estimators for network meta-analysis with contrast-based approach

##article.authors##

  • Hisashi Noma Department of Data Science, The Institute of Statistical Mathematics

DOI:

https://doi.org/10.51094/jxiv.490

Keywords:

network meta-analysis, contrast-based approach, multivariate random-effects model, within-study covariance matrix, network meta-regression

Abstract

The contrast-based approach is one of the primary approaches in network meta-analysis. For statistical modeling in network meta-analysis and meta-regression models, within-study covariance estimates are needed to adequately address the correlations among the multivariate outcomes. In this computational note, we present the formulas of covariance estimators for standard effect measures used in modern meta-analysis practice: risk difference, risk ratio, odds ratio, mean difference, and standardized mean difference (Cohen's d and Hedge's g).

Conflicts of Interest Disclosure

The authors have no conflict interest to declare.

Downloads *Displays the aggregated results up to the previous day.

Download data is not yet available.

References

DerSimonian, R., and Laird, N. M. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials 7, 177-188.

Greenland, S. (1987). Interpretation and choice of effect measures in epidemiologic analysis. American Journal of Epidemiology 125, 761-768.

Hedges, L. V. (1981). Distributional theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics 6, 107-128.

Hedges, L. V., and Olkins, I. (1985). Statistical Methods for Meta-Analysis. New York: Academic Press.

Higgins, J. P. T., and Thomas, J. (2019). Cochrane Handbook for Systematic Reviews of Interventions, 2nd edition. Chichester: Wiley-Blackwell.

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

Noma, H. (2023). Bayesian estimation and prediction for network meta-analysis with contrast-based approach. International Journal of Biostatistics, DOI: 10.1515/ijb-2022-0121.

Noma, H., Hamura, Y., Gosho, M., and Furukawa, T. A. (2023a). Kenward-Roger-type corrections for inference methods of network meta-analysis and meta-regression. Research Synthesis Methods, DOI: 10.1002/jrsm.1652.

Noma, H., Hamura, Y., Sugasawa, S., and Furukawa, T. A. (2023b). Improved methods to construct prediction intervals for network meta-analysis. Research Synthesis Methods, DOI: 10.1002/jrsm.1651.

Noma, H., Nagashima, K., and Furukawa, T. A. (2020). Permutation inference methods for multivariate meta-analysis. Biometrics 76, 337-347.

Noma, H., Nagashima, K., Maruo, K., Gosho, M., and Furukawa, T. A. (2018). Bartlett-type corrections and bootstrap adjustments of likelihood-based inference methods for network meta-analysis. Statistics in Medicine 37, 1178-1190.

Salanti, G., Higgins, J. P., Ades, A. E., and Ioannidis, J. P. (2008). Evaluation of networks of randomized trials. Statistical Methods in Medical Research 17, 279-301.

Student (1908). The probable error of a mean. Biometrika 6, 1-25.

White, I., and Thomas, J. (2005). Standardized mean differences in individually-randomized and cluster-randomized trials, with applications to meta-analysis. Clinical Trials 2, 141-151.

White, I. R. (2015). Network meta-analysis. Stata Journal 15, 951-985.

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.

Whitehead, A. (2002). Meta-Analysis of Controlled Clinical Trials. Chichester: Wiley.

Downloads

Posted


Submitted: 2023-08-19 16:49:22 UTC

Published: 2023-08-24 02:18:52 UTC
Section
General Medicine, Social Medicine, & Nursing Sciences