User Manual Meta is a C11 tiny metaprogramming library developed by Eric Niebler to facilitate the computation and manipulation of types and lists of types (aka, variadic parameter packs). It is released under the Boost Software License and it is header only; that is, to compile with meta you just have to: #include meta/meta.hpp. Meta-Guide.com is a semi-automated research machine, using artificial intelligence techniques to study A.I. (which is a prerequisite for singularity). Meta Guides: 100 Best Support Vector Machine Videos.
Introduction
This is the manual for the GeMTC user interface for network meta-analysis. It starts with a brief introduction to network meta-analysis in the Bayesian framework, including issues such as model fit and convergence. This is followed by a guide to the GeMTC user interface itself. The GeMTC user interface uses question mark icon to provide help for specific terms, which when clicked will show a brief explanation as well as a link to further information in this manual.
Although this manual contains a brief introduction to the terminology and methodology used in GeMTC, it is not a complete guide to network meta-analysis. For further background, we recommend the following excellent open access publications:
- The Medical Decision Making series on network meta-analysis based on the NICE Decision Support Unit series on evidence synthesis: Table of Contents, MDM 2, MDM 3, MDM 4
- The ISPOR guidelines on network meta-analysis: ISPOR 0, ISPOR 1, ISPOR 2
- Evaluating the quality of evidence from a network meta-analysis: Salanti et al. (2014)
GeMTC is available as a stand-alone package at gemtc.drugis.org, and is also part of the ADDIS decision support system for evidence based medicine, developed at drugis.org. The functionality in the GeMTC user interface is supported by the GeMTC R package. All drugis.org software is open source, and source code is available on github.com/drugis.
Preparing your dataset
This section only applies to the stand-alone version of GeMTC, hosted on https://gemtc.drugis.org/. In ADDIS ( https://addis.drugis.org/), this step is not necessary.
After signing in to GeMTC, you will be redirected to your personal home page. It contains a list of your previously created analyses (which will be empty until you create one), and a button to create a new analysis. Clicking this button will open the “New analysis” dialog, where you choose a title for your analysis and outcome, and upload a dataset file:
Datasets can be uploaded in CSV format. The CSV file should contain one row per arm, and needs to contain “study” and “treatment” columns, which can contain names or identifiers for the study and treatment. The data columns may be one of:
- Continuous data: “mean” and “std.err”
- Continuous data: “mean”, “std.dev”, and “sampleSize”
- Dichotomous data: “responders” and “sampleSize”
- Survival data: “responders” and “exposure” (exposure in person-time)
The following is an example CSV file for a Parkinson’s disease dataset:
Including covariates in the dataset
To include covariates in a dataset, simply add additional columns to the CSV file, but do not to use any of the column names already recognized by GeMTC (study, treatment, mean, etc.). Covariates are currently only considered at the study level. Therefore, you can include these data in one of two different ways:
- Specify the covariate value for only one of the study arms, and leave it as missing or 'NA' for the others.
- Specify the covariate value for all arms of the study, making sure that each arm within a study has the same value as the other arms.
Including contrast-based data
Contrast based data (e.g. log odds ratios) can also be analyzed in GeMTC. Datasets can contain both contrast-based and arm-based data, but any single study must be either completely contrast-based or completely arm-based. Contrast based data are effect estimates and their standard errors on a specific scale (e.g. mean difference or log-odds ratio); that scale must be specified when uploading contrast-based data.
Contrast-based data are also specified using the one arm per row format. For every arm except the base arm, specify the effect estimate and its standard error in the columns 're.diff' and 're.diff.se'. For the base arm, these columns must be left as missing or 'NA'.
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In multi-arm (that is, more than 2 arms) trials, the treatment contrasts are correlated. This must be accounted for in the likelihood. To do this, the standard error of the (absolute) effect in the base arm must be specified in the column 're.base.se'. For example, if the outcome scale is the log-odds ratio, this is the standard error of the absolute log-odds in the base arm. By a convenient statistical coincidence, the standard error of the base arm squared is equal to the covariance between the treatment contrasts. Thus, if the covariance is known, set 're.base.se' to the square root of the covariance.
There are a number of caveats when using contrast-based data. First, as the number of participants is not known for this data, the size of nodes in the evidence graph does not reflect the number of participants. Second, the scale at which the relative effects were computed will restrict the available likelihoods. Third, model fit statistics for contrast-based data are not computed at the arm level, but at the study level.
The workbooks and a pdf-version of this user manual can be downloaded from here.
This is the user manual for Meta-Essentials, a set of workbooks for meta-analysis. The workbooks, as well as this manual are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. That means you can use, share, and adapt the tools all you want, as long as you properly attribute the original effort to us.
If you use this tool for any type of publication, please cite it as follows:
- Suurmond R, van Rhee, H, Hak T. (2017). Introduction, comparison and validation of Meta-Essentials: A free and simple tool for meta-analysis. Research Synthesis Methods. Vol. 8, Iss. 4, 537-553. https://doi.org/10.1002/jrsm.1260.
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However, if you refer to the material on the website or the user manual in particular, please cite as follows:
- Van Rhee, H.J., Suurmond, R., & Hak, T. (2015). User manual for Meta-Essentials: Workbooks for meta-analysis (Version 1.2) Rotterdam, The Netherlands: Erasmus Research Institute of Management. Retrieved from www.erim.eur.nl/research-support/meta-essentials
Aim of this user manual
This user manual is a guide for the usage of Meta-Essentials. It is not a guide on how you should search for studies, which studies you should include, nor for how the results of the meta-analysis should be interpreted. We have written a separate text on these matters (see Hak, Van Rhee, & Suurmond, 2015). We have also published a paper that describes Meta-Essentials and how it compares to other tools for meta-analysis (Suurmond, van Rhee, and Hak 2017).
Structure
The user manual for Meta-Essentials is organized as follows:
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- Select the appropriate workbook
2.1 Effect sizes of the d-family
2.2 Effect sizes of the r-family - Work with the workbooks
3.1 Input sheet
3.2 Forest Plot sheet
3.3 Subgroup Analysis sheet
3.4 Moderator Analysis sheet
3.5 Publication Bias Analysis sheet
3.5.1 Funnel plot
3.5.2 Egger regression and Begg and Mazumdar rank correlation test
3.5.3 Standardized Residual Histogram
3.5.4 Galbraith Plot
3.5.5 Normal Quantile plot
3.5.6 Failsafe-N tests
3.6 Calculations sheet
3.7 Statistical procedures - Specific features of individual workbooks
4.1 Workbook 2
4.2 Workbook 3 & 4
4.3 Workbook 5
4.4 Workbook 6 & 7
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The first step when using Meta-Essentials is to choose the appropriate workbook for the meta-analysis. Then, this manual discusses how to insert data, how to perform a basic meta-analysis and to generate a forest plot, how to run a subgroup analysis, a moderator analysis, and various publication bias analyses. Also, the calculations 'behind' the sheets and the applied statistical methods are discussed, however, knowledge or understanding of these methods is not required for using Meta-Essentials. Next, the manual discusses those instructions that apply only to specific workbooks. This manual concludes with discussing guidance for how output of Meta-Essentials can be adapted for inclusion in a report.
Compatibility
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The workbooks of Meta-Essentials are compatible with Microsoft Excel 2010, 2013 and 2016. Older versions of Microsoft Excel might work fine in some cases, but some formulas and formatting features are not supported by these older versions. Although we designed Meta-Essentials for Microsoft Excel, it also works with the freely available WPS Office 2016 Free and Microsoft Excel Online (free registration required). The workbooks of Meta-Essentials are unfortunately not compatible with OpenOffice or Google Docs. These programs do not correctly calculate all formulas and cannot display all figures.