Note that the package requires the latest versions of metan, mvmeta and network and is compatible with Stata versions 13, 14 and 15. The metamiss2 command performs a two-stage approach: it first estimates the ‘adjusted’ study-specific relative effects and their variances and covariances, and then calls metan or network meta to obtain the summary effects. Recently, the IMP framework was extended into meta-analyses with continuous outcomes.
In Stata, information about missing data can be incorporated in meta-analyses of binary outcomes using the metamiss command. The use of informative missingness parameters (IMP) that relate the outcome in the missing data with that in the observed data has been previously suggested for handling missing outcome data in meta-analysis of binary outcomes. Researchers typically ignore missing data and analyze complete data only this approach is equivalent to assuming that missing participants are missing at random (MAR). Missing outcome data are a common threat to the validity of randomized trials and their meta-analysis, as they require making untestable assumptions. The metamiss2 package – Accounting for missing outcome data in meta-analysis With Stata running and internet connection available, the latest versions of the packages can be downloaded by typing in the command window:įor questions and comments please contact Dr. Graphical Tools for Network Meta-Analysis in STATA. Stata Journal 2015 15(4): 905-950.Ĭhaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. Visualizing assumptions and results in network meta-analysis: the network graphs package. Allowing for uncertainty due to missing data in meta-analysis-Part 1: Two-stage methods. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis. Mavridis D, White IR, Higgins JPT, Cipriani A, Salanti G.
Allowing for informative missingness in aggregate data meta-analysis with continuous or binary outcomes: Extensions to metamiss. If you use it, please kindly cite the articles that describe them:Ĭhaimani A, Mavridis D, Salanti G, Higgins JPT, White IR.
We hope you will find the software presented useful.