MIX 2.0 Lite and Pro
MIX 2.0 Lite and Pro are very much alike when it comes to analytical functions. The big difference is the data that you can analyze. MIX Lite is meant for learning and teaching and therefore only allows the user to analyze built-in datasets. It also does not include some of the extra modules that are developed for MIX 2.0 Pro.
|
Feature
|
Lite
|
Pro
|
|---|---|---|
|
O
|
O
|
|
|
X
|
O
|
|
| Convert effect sizes |
X
|
O
|
| Numerical meta-analysis |
O
|
O
|
| Graphical meta-analysis |
O
|
O
|
O = available, X = unavailable
MIX 2.0 general features
Load built-in datasets (Lite & Pro)
MIX Lite and Pro both contain a large number (currently 20+) built-in data sets. The data sets are from authoritative books on meta-analysis and will be expanded with data sets from methodological articles. MIX can therefore be used to reproduce and expand on the analyses and examples presented in these books. The dataset file is regularly updated and will be available for free download from the MIX 2.0 website.
Create custom datasets (Pro)
MIX 2.0 Pro contains a datawizard that provides a number of options to create custom datasets. The datasets can be of three types:
- dichotomous outcome data (events and non-events)
- continuous outcome data (means, standard deviations and group sizes)
- generic data (study estimate with standard error)
We are currently experimenting with creating another separate
option for correlation data or using the data convertor to transform
such data to the generic data type described above.
The data can be entered in a dataset template, either manually or via copy-paste procedures or they can be imported in spreadsheet format. In the latter case, MIX 2.0 Pro will create the dataset after the user has identified the rows that contains the data. MIX 2.0 Pro will soon be able to import datasets that were created in RevMan 4 and RevMan 5.
Convert effect sizes (Pro)
The data conversion utility in the Data Wizard in MIX 2.0 Pro enables users to convert data types and variability measures for use in MIX 2.0 Pro. If a study provides only a correlation coefficient and a sample size, the data can currently not be entered directly in MIX 2.0. However, the converter can calculate Fisher's Z and its standard error and make the data compatible with the MIX data entry options. A similar thing can be done for non-comparative rates and sample sizes (proportions), which can be converted with a Freeman-Tukey double arcsine transformation. The converter can also be useful if a study only provides an estimate with a confidence interval, but you really want the estimate and its standard error. The convertor can use data of a single study at a time or apply the conversion to a number of studies at once.
Numerical meta-analysis (Lite & Pro)
The basic numerical meta-analysis possibilities in MIX 2.0 Lite and Pro are identical. The available feature set is very comprehensive and conveniently categorized in exploration, synthesis, and evaluation procedures.
|
Numerical
meta-analysis features |
Lite |
Pro |
|---|---|---|
| Exploration | ||
| Data set overview | O |
O |
| Data set summary (studies, N, event scarcity, etc) |
O |
O |
| Heterogeneity statistics (Q with P, I^2 with CI, tau^2 with CI) |
O |
O |
| Synthesis | ||
| Fixed effect models |
O |
O |
| - Inverse variance |
O |
O |
| - Mantel-Haenszel |
O |
O |
| - Peto |
O |
O |
| Random effects models |
O |
O |
| - DerSimonian-Laird one-step |
O |
O |
| - DerSimonian-Laird two-step |
O |
O |
| - Paule-Mandel |
O |
O |
| Binary outcome data | ||
| - Odds ratio (OR) |
O |
O |
| - Risk ratio (RR) |
O |
O |
| - Risk difference (RD) |
O |
O |
| Continuous outcome data measures | ||
| - Mean difference (MD) |
O |
O |
| - Hedges' G |
O |
O |
| - Cohen's D |
O |
O |
| Generic data entry measures | ||
| - Any measure with its standard error |
O |
O |
| - Back transformation of log, logit, arcsine, and Fisher's z values | O |
O |
| Confidence intervals for synthesis result | ||
| - Z-distribution |
O |
O |
| - T-distribution |
O |
O |
| - Bootstrap |
C |
C |
| Continuity corrections for non-event groups | ||
| - Custom constant value |
O |
O |
| - Arm-dependent values |
O |
O |
| Cumulative synthesis |
O |
O |
| Subgroup synthesis | O |
O |
| Bayesian synthesis | O |
O |
| Evaluation | ||
| Exclusion sensitivity assessments |
O |
O |
| - Single study exclusions |
O |
O |
| Dissemination selectivity statistics |
O |
O |
| - Rank correlation tests (Begg & Mazumdar / Rucker) |
O/C |
O/C |
| - Regression tests (Egger, Macaskill, Peters, Harbord) |
O |
O |
| Dissemination bias analysis |
O |
O |
| - Regression methods (Peters, Egger) |
O |
O |
| - Trim and fill analysis |
O |
O |
O = available, C = under construction
Graphical meta-analysis (Lite & Pro)
The basic graphical meta-analysis possibilities in MIX 2.0 Lite and Pro are identical. MIX 2.0 has the largest arsenal of graphs of all meta-analysis software. The available graphical options are not only very comprehensive, the graphs can also be customized easily via the right-click dialog boxes of Excel 2007. Alternatively, the graphs can be exported to PowerPoint 2007 and, with the vector-based graph items ungrouped, the graph can be adjusted in any way imaginable. The vector-based graphs from MIX result in high-quality imagines no matter how large they are made (the vector-based properties prevent pixelation) and are therefore ideal to create publication-quality graphs. Most journals accept such PowerPoint vector-based image files directly, but they are also easily imported and adjusted in graphical software such as Adobe Photoshop, Adobe Illustrator, or free alternatives such as Gimp and Paint.NET.
|
Graphical
meta-analysis features |
Lite |
Pro |
|---|---|---|
| Exploration | ||
| Simple forest plot |
O |
O |
| Histogram |
O |
O |
| Normal-quantile plot |
O |
O |
| Galbraith plot |
O |
O |
| L'Abbe plot |
O |
O |
| Heterogeneity funnel plot |
O |
O |
| Baujat plot |
O |
O |
| Synthesis | ||
| Synthesis forest plot |
O |
O |
| Cumulative forest plot |
O |
O |
| Subgroup synthesis forest plot | C |
O |
| Bayesian triplot | O |
O |
| Evaluation | ||
| Exclusion sensitivity plot |
O |
O |
| Weighting sensitivity plot |
C |
O |
| Selectivity funnel plot |
O |
O |
| Selectivity box plot | C |
O |
| Selectivity regression plot |
O |
O |
| Trim and fill plot |
O |
O |
O = available, X = unavailable, C = under construction