We hope you will find the software presented useful. If you use it, please kindly cite the articles that describe them:

*For the ‘metamiss2’ package*

*Chaimani A, Mavridis D, Salanti G, Higgins JPT, White IR. Allowing for informative missingness in aggregate data meta-analysis with continuous or binary outcomes: Extensions to metamiss. Stata J 2018; 18(3):716-740*

*Mavridis D, White IR, Higgins JPT, Cipriani A, Salanti G. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis. Stat Med 2015; 34: 721–741*

*White IR, Higgins JPT, Wood A. Allowing for uncertainty due to missing data in meta-analysis—Part 1: Two-stage methods. Stat Med 2008; 27:711–727*

*For the ‘network graphs’ package*

*Chaimani A, Salanti G. Visualizing assumptions and results in network meta-analysis: the network graphs package. Stata Journal 2015; 15(4): 905-950. *

*Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. **Graphical Tools for Network Meta-Analysis in STATA. **PLoS One. 2013 Oct 3;8(10):e76654.*

* *

**How to install the Stata packages**

With Stata running and internet connection available, the latest versions of the packages can be downloaded by typing in the command window:

. net from http://clinicalepidemio.fr/Stata

and clicking at the respective link.

For questions and comments please contact **Dr. Anna Chaimani** anna.chaimani@parisdescartes.fr

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**Description**

**1. The metamiss2 package – Accounting for missing outcome data in meta-analysis**

Missing outcome data are a common threat to the validity of randomized trials and their meta-analysis, as they require making untestable assumptions. Researchers typically ignore missing data and analyze complete data only; this approach is equivalent to assuming that missing participants are missing at random (MAR). 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.

In Stata, information about missing data can be incorporated in meta-analyses of binary outcomes using the metamiss command. Recently, the IMP framework was extended into meta-analyses with continuous outcomes. 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.

*Latest update: 01 October 2018*

To access the help file type:

. help metamiss2

Note that the package requires the latest versions of metan, mvmeta and network and is compatible with Stata versions 13, 14 and 15.

**2. ****The network graphs package – Visualizing assumptions and results in network meta-analysis**

Although network meta-analysis has been established as a useful evidence synthesis tool it has been often criticized for its complexity and for been accessible only to researchers with strong statistical and computational skills. Careful evaluation of its assumptions and understandable, concise presentation of the results are necessary to avoid misinterpretations and inform decision-making. We provide a series of Stata commands that can be used to produce useful graphical and numerical tools to enhance understanding of network meta-analysis procedures and findings.

*Included commands*

*Latest update of the commands: 24 May 2019*

networkplot | Plot for networks of interventions in terms of nodes and edges |

netweight | Contribution of each direct comparison in network meta-analysis estimates |

ifplot | Evaluation of statistical inconsistency in networks of interventions |

netfunnel | Comparison-adjusted funnel plot for a network of interventions |

intervalplot | Confidence & Predictive intervals plot |

netleague | League table for networks of interventions |

sucra | Ranking plots for a single outcome of network meta-analysis using probabilities of assuming up to a specific rank |

mdsrank | Ranking of treatments in networks of interventions using multidimensional scaling |

clusterank | Clustering for treatments of a network of interventions according to their performance on two outcomes |

To access the help files type:

. help network graphs

Note that the package requires the latest versions of metan, metareg, mvmeta and network and are compatible with Stata versions 13, 14 and 15.