Comprehensive Meta-Analysis

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What is a Meta-Analysis?


Meta-analysis is the statistical procedure for combining data from multiple studies. When the treatment effect (or effect size) is consistent from one study to the next, meta-analysis can be used to identify this common effect. When the effect varies from one study to the next, meta-analysis may be used to identify the reason for the variation.


Why do a Meta-Analysis?


Decisions about the utility of an intervention or the validity of a hypothesis cannot be based on the results of a single study, because results typically vary from one study to the next. Rather, a mechanism is needed to synthesize data across studies. Narrative reviews had been used for this purpose, but the narrative review is largely subjective (different experts can come to different conclusions) and becomes impossibly difficult when there are more than a few studies involved. Meta-analysis, by contrast, applies objective formulas (much as one would apply statistics to data within a single study), and can be used with any number of studies.


Meta-Analysis in applied and basic research


Pharmaceutical companies use meta-analysis to gain approval for new drugs, with regulatory agencies sometimes requiring a meta-analysis as part of the approval process. Clinicians and applied researchers in medicine, education, psychology, criminal justice, and a host of other fields use meta-analysis to determine which interventions work, and which ones work best. Meta analysis is also widely used in basic research to evaluate the evidence in areas as diverse as sociology, social psychology, sex differences, finance and economics, political science, marketing, ecology and genetics, among others.


For the Researcher


CMA is incredibly easy to learn and use, with a clear and intuitive interface. The interactive guide will walk you through all steps in the analysis, allowing new users to be productive within minutes.


For the Statistician


CMA was developed in collaboration with many of the recognized experts in the field of meta-analysis, both in the US and the UK. It includes a wide array of sophisticated options for data entry, analysis, and display.


For the Academic instructor


With CMA, the logic of meta-analysis comes alive. Use the program to help explain complex issues, such as the impact of study weights on the combined effect, the implications of heterogeneity, or the distinction between fixed effect and random effects models.


Comprehensive Meta-Analysis will allow you to:


Work with a spreadsheet interface
Compute the treatment effect (or effect size) automatically
Perform the meta-analysis quickly and accurately
Create a high-resolution forest plot with a single click
Perform a cumulative meta-analysis
Perform a sensitivity analysis
Assess the impact of moderator variables
Work with multiple subgroups or outcomes within studies
Assess the potential impact of publication bias
Work with subsets of the data


Comprehensive Meta Analysis is now available in 3 editions(目前共有以下三种版本)


1.“Lite” edition
• Work with a spreadsheet interface
• Work with odds ratios, risk ratios, risk differences, mean differences, standardized mean differences, and correlations
• Enter data for each study in its own format
• More than 30 data formats included
• Compute the treatment effect (or effect size) automatically
• Use ANOVA to assess the impact of moderator variables
• Create high-resolution forest plots in Black and White and export to Word


2.Standard edition


• All features in Lite edition, plus:
• Create high-resolution forest plot in color for export to PowerPoint


3.Professional edition


• All features in Standard edition, plus:
• Work also with hazard ratios, ratios of events by person years, risks in one group, mean in one group, generic effects sizes, and more
• Enter data for each study in its own format – more than 100 formats included
• Use meta-regression to assess the impact of continuous moderator variables
• Use various mechanisms to assess the impact of publication bias
• Work with multiple independent subgroups within studies
• Work with multiple outcomes, time-points, or comparisons within studies.



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