Comprehensive Meta Analysis V2 [VERIFIED] Crack
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Meta-analysis has become a well-known method for synthesis of quantitative data from previously conducted research in applied health sciences. So far, meta-analysis has been particularly useful in evaluating and comparing therapies and in assessing causes of disease. Consequently, the number of software packages that can perform meta-analysis has increased over the years. Unfortunately, it can take a substantial amount of time to get acquainted with some of these programs and most contain little or no interactive educational material. We set out to create and validate an easy-to-use and comprehensive meta-analysis package that would be simple enough programming-wise to remain available as a free download. We specifically aimed at students and researchers who are new to meta-analysis, with important parts of the development oriented towards creating internal interactive tutoring tools and designing features that would facilitate usage of the software as a companion to existing books on meta-analysis.
The amount of data produced by researchers in health sciences has been growing explosively and advances in genetics, genomics, and information technology are likely to further contribute to this growth. In the past two decades, meta-analysis has evolved into the statistical method par excellence to make sense out of the growing number of research reports. As the quantitative analytical part of a systematic review, it has been used for evaluating data from both experimental and observational studies in therapeutic, diagnostic, prognostic, and etiologic settings. In the commonly used definition of the hierarchy of scientific data for medical decision making, meta-analyses are considered as providing the highest level of evidence [1, 2]. As such, they can have a major impact on medical practice and health care policies, especially if aggregating data and investigating sources of heterogeneity provide new insights. Two well-known examples are the meta-analyses by Yusuf et al  and Lau et al , both showing that meta-analysis can be a powerful tool to show intervention effects that would remain beneath the surface of single study data without proper synthesis and re-analysis.
Many general statistical software packages have included options for meta-analysis in their basic program configuration, and user-communities have written numerous meta-analysis add-ons. Specialized software packages, meant exclusively for meta-analysis, are also available in various types and price ranges. Although the number of software packages for performing meta-analysis is substantial, in our opinion, most share one common limitation: low applicability in educational settings or environments with beginning researchers. Even though numerous researchers in health care are nowadays confronted with data from published meta-analyses or are even requested to do a meta-analysis themselves, there is still little or no electronic educational material and none of the existing software has explicit educational features. Cost is another issue that may have an impact on the use of software by students and lecturers: only a few of the modern meta-analysis packages are free and if academic pricing is available, prices can still be rather high for many.
Our primary objective was to develop a free program for meta-analysis of causal research (therapeutic trials as well as etiologic cohorts and case-control studies) that could be applied in both analytical and educational settings. Our secondary aim was to validate the analytical tests in the program with output from established reference standards.
Structure of the MIX program. The MIX program is started by simply clicking the MIX icon on the desktop or in the Windows Start Menu. The program uses a number of Excel workbooks, of which only the output file (*) is directly accessible by the user. Via the custom interface, several educational features can be accessed and custom meta-analysis reports can be produced.
The MIX program provides several options for importing or creating data sets for meta-analysis. The most convenient option is to create an Excel or CSV file with data (standard output option in Excel) and import this file into the MIX program. The variable ranges are then selected in Excel-manner to create a data set (see figure 4), which is subsequently loaded for analysis and optionally saved as a MIX data set file (*.mxd). The program accepts descriptive data from studies with continuous outcomes, e.g. sample size, mean, standard deviation, and dichotomous outcomes, e.g. group sizes and event numbers (two-by-two table data). Comparative data can also be loaded by means of association measures with their standard error. Initially, however, it is not necessary to make a data set since 19 data sets from the most authoritative books on the subject ("Meta-analysis in Medical Research" by Sutton et al , "Systematic Reviews in Health Care, Meta-Analysis in Context" by Egger et al , and Systematic Reviews in Health Care, A Practial Guide by Glasziou et al ) have been included in the program. Most analyses and graphs presented in these books can be reproduced with a few clicks and the program can be used as a learning or teaching companion to these books. We hope to support more more books in this way in the future. In addition, the MIX website also contains a data set repository where users can contribute and download MIX data sets.
A large variety of numerical and graphical output can be produced by the program. Besides the association measure values from the meta-analysis, several formal tests for heterogeneity, small study effects (publication bias), single study influence, and cumulative trends are also available in MIX. The graphical output is particularly comprehensive, with no less than eighteen informative plots that can be formatted in detail.
The most important educational features are the program's Output Tutor and Concept Tutor. Both are interactive dialog boxes that provide information about epidemiological and statistical concepts and tests. The Output Tutor changes with each analysis and always explains tests and results that are displayed or changed at the very moment. Additional teaching material includes a Flash-based Theory Tour that explains the fundamentals of systematic reviews and meta-analyses and a Program Tour that shows the basics of how to use the program. The educational materials take up approximately 25 Mb and can also be downloaded separately.
The data sets represent three of the most often used types of data for meta-analysis in health care research: 1) descriptive data for dichotomous outcomes, 2) descriptive data for continuous outcomes, and 3) comparative (association measure) data. For all three data types we chose a relatively small (less than 10 studies) and large data set (more than 20 studies) and we used two extra data sets in the 'descriptive dichotomous' category (one representing a meta-analysis of substantially heterogeneous studies and one with a rare event). The data sets are summarized in table 2. The tests that were subject to the validation procedures are shown in table 3. The items include individual study association measures, combined association measures, and several heterogeneity and small study effect assessments. Whenever applicable, p-values and/or confidence intervals were also compared.
In summary, we have been able to achieve our objective of developing a comprehensive and yet free program for meta-analysis. The Excel platform, although not without problems, has proved to be flexible enough to create an easy-to-use, and graphically and numerically comprehensive program.
With regard to the trim-and-fill analysis , the MIX program allows for calculations using the weighting method applied in the original meta-analysis, whereas both CMA and STATA use only fixed or random effects inverse variance methods when trimming and filling. While the calculations in MIX for trim-and-fill analyses with other weighting methods were verified manually and we have no reason to believe anything is wrong, we recommend using the inverse variance methods until more is known about approaches with alternative weighting.
Although we are in the process of completing a formal software comparison project, we are confident that the MIX program can compete in many respects (usability, analytical options, comprehensiveness, and export options) with most of the existing meta-analysis programs like Comprehensive Meta-Analysis , MetaWin , RevMan , or WEasyMA . However, there are also still some limitations. One is the maximum number of studies that can be analyzed in the meta-analysis, which is now 100. Though systematic reviews finding 100 studies for analysis are still very rare, this is something that may change in the future. Furthermore, while sub-group analyses are easy to perform within MIX, they are currently not automated and during a sub-group analysis not all subgroups can be shown simultaneously in a single forest plot. The subgroup forest plot can however be created manually because the Excel graphs of individual forest plots are relatively easily formatted and stacked. We intend to improve the program with regard to these limitations in the near future.
Another important issue that we will focus on in upcoming updates is meta-regression. Although some univariable regression methods are integrated in the tests for small study effects, the MIX program can currently not perform meta-regression. We realize that meta-regression, especially with multiple independent variables, is a valuable tool for assessing heterogeneity and adapting a meta-analysis accordingly, but it requires matrix calculations that are far more difficult to program in Excel or VBA than the standard tests. Currently, univariable meta-regression is possible with Comprehensive Meta-Analysis  and MetaWin . However, like all dedicated meta-analysis packages they lack the option for multivariable meta-regression. We have started working on facilities for meta-regression within the MIX program and we hope it will be integrated sometime in 2007. 153554b96e