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MetaXL keeps pushing the envelope of innovation in meta-analysis. Version 1 introduced the quality effects (QE) model, version 2 the inverse variance heterogeneity (IVhet) model, version 3 introduced the Doi plot and LFK index for the detection of publication bias, version 4 added network meta-analysis. Now version 5 adds cumulative meta-analysis to this already rich list of features
Meta-analysis is a statistical method to combine the results of epidemiological studies in order to increase power. Basically, it produces a weighted average of the included studies results.
There are two main issues with meta-analysis: heterogeneity between studies, and publication bias. Heterogeneity is usually dealt with by employing the random effects (RE) model. However the RE estimator, as explained in the MetaXL User Guide, underestimates the statistical error and has a larger mean squared error (MSE) than even the fixed effects estimator. It also makes unjustifiable changes to study weights. For these reasons it is seriously flawed and should be abandoned. MetaXL offers two alternatives to the RE model:
1) The IVhet model provides a quasi-likelihood based expansion of the confidence interval around the inverse variance weighted pooled estimate when studies exhibit heterogeneity (without inappropriate changes to individual study weights, as the random effects model does), thus keeping the MSE lower than with the random effects estimator.
2) The QE model allows incorporating information on study quality into the analysis, thereby affording the opportunity for further reduction in estimator MSE beyond that of the IVhet model. Much of the heterogeneity between study results is explained by differences in study quality, and it is preferable to make use of this information explicitly.
More background on these alternatives is in our publications.
Publication bias can occur, among other reasons, because studies with ‘positive’ results are more likely to get published than ones with ‘negative’ results. Traditionally, the funnel plot is used to detect possible publication bias, but this plot is often hard to interpret. MetaXL now offers an alternative, the Doi plot, which is much easier to interpret.
Network meta-analysis can make multiple indirect comparisons, thus allowing to assess a range of treatment options against a common comparator. It is a powerful technique, but it has been held back by complex methods. The MetaXL implementation is powerful, yet very easy to use.
Cumulative meta-analysis allows to analyse how the evidence evolved over time.
Using Excel as a platform makes MetaXL-based meta-analysis highly accessible. And you still can’t beat the price!

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Install the IJ Scan Utility. Once the download is complete, double-click the downloaded file. A disk image will be placed on the desktop. Open this file, then open the.pkg (package) file to begin the driver installation. Follow the prompts to complete installation. After installing the appropriate driver, go to the Software tab and find the IJ. Sync for Windows. Upload, sync and share files and folders from your Windows desktop, laptop or tablet. Supports Windows 7, 8, 10. Download for Windows. When your download is complete, run the Sync installer and follow the prompts. For help installing on Windows click here, or view 2.0.17 release notes. Visit developer's site Download MetaX 2.77 18.4MB Win Software License. Shareware (Free download but time limited software. Full version from $10) Supported operating systems. Mac = Mac download version. It works on 32-bit and 64-bit Mac OS. Mac64 = Mac OS download version. It works only on 64-bit Mac.

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IMPORTANT UPDATE -- 04/06/2019
A recent glitch has been discovered whereby two separate sets of studies with the exact same pooled effect size and standard error produce Doi plots and LFK indexes that do not overlap with each other. This is due to an error in the ranking calculation within MetaXL. This glitch only occurs when the same effect size and standard error are observed across different sets of studies.
A Stata ado file has been developed to generate a Doi plot and LFK index without the glitch. This can be accessed by downloading LFK Stata package v1.zip. The downloaded file contains: (1) a Stata ado file implementing the fix; (2) a Stata help file; and (3) a PDF which describes the problem in full and provides accompanying installation instructions.
Please note that this fix is an alpha version as it only deals with the IOType parameters: ContSE; NumOR; and NumRR.