最新版 Comprehensive MetaAnalysis V3
Comprehensive MetaAnalysis v3版 開始全面改為年租授權（許可期限：一年或兩年）
Metaanalysis 基本概念
Metaanalysis 方法是依靠搜集已有或未發表的具有某一可比特性的文獻，應用特定的設計和統計學方法進行分析與綜合評價，使有可能對具有不同設計方法及不同病例數的研究結果進行綜合比較。
為何要做 Metaanalysis?
Metaanalysis方法主要解決以下的問題：
 增加統計功效：提高對初步結論的論證強度及臨床所見效應的分析評估力度。由於單個臨床試驗往往樣本太小，難以明確肯定某種效應，而這些效應對臨床來說又可能是重要的。如果要求從統計學上來肯定或排除這些效應，則需要較大的樣本，而若採用 Metaanalysis 方法要比1項大規模、代價高昂甚或不切實際的研究更為可行，而且把許多具有可比性的單個臨床試驗結果進行合併分析，可以改善對效應的估計值或把握度。
 解決各研究結果的不一致性：對同1個研究問題，各個臨床試驗結果可能不盡一致，甚或存在分歧爭議，利用 Metaanalysis 方法可以得到對該問題的全面認識，並作出科學的結論。
 尋求新的假說：Metaanalysis 方法可以回答單個臨床試驗中尚未提及或是不能回答的問題，尤其用於隨機對照試驗設計所得的結果進行綜合評價，可以提出一些尚未研究的新問題。
為何要使用CMA軟體？

對於研究員（For the Researcher）
CMA具有明確和直觀的界面，非常容易學習和使用。互動式指南將引導您完成所有分析步驟，使新用戶能夠在幾分鐘之內將產出結果。


對於統計員（For the Statistician）
CMA的開發藉著與許多公認metaanalysi領域的專家合作，無論是在美國和英國。CMA包括如數據輸入、分析和顯示等廣泛的選項。


對於學校老師（For the Academic Instructor）
藉由CMA可讓metaanalysis的邏輯變得生動起來。使用CMA程式可以幫助解釋複雜的問題，如影響學習的加權綜合因素，其影響的異質性或之間的區別固定效應和隨機效應模型。


對於研究生（For the Graduate Student）
可以幫助您在十五分鐘內完成一個基本的分析。其創建和導出的結果，可以作為分析框架，用來充分了解分析的邏輯。

新版更新功能
Version 3 includes a new module for metaregression that
 Allows you to include any number of covariates
 Allows you to define sets of covariates
 Allow you to include both categorical and continuous covariates in the model
 Will automatically create dummy variables for categorical covariates
 Allows you to define and compare multiple predictive models
 Allows you to choose either the Zdistribution or KnappHartung
 Allows you to plot the regression, as well as confidence and prediction intervals
 Automatically plots the Rsquared analog
 Allows oneclick export of data to Excel
 Allows oneclick export of plots to PowerPoint and Word
Effect size for clusterrandomized studies
 With a license for CMA, we provide a license for a second program that allows you to compute the effectsize and variance for clusterrandomized and other multilevel studies.
Other features
 A number of updates that allows the program to work better with Windows 7 and 8.
New licensing scheme
 We will no longer offer a perpetual license. Beginning with Version 3, you can lease the program for one or two years.
Comprehensive MetaAnalysis 10 大功能
 Work with a spreadsheet interface
 Compute the treatment effect (or effect size) automatically
 Perform the metaanalysis quickly and accurately
 Create a highresolution forest plot with a single click
 Perform a cumulative metaanalysis
 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
Work with a spreadsheet interface
Enter data directly or import data from other program
You can type data directly into the spreadsheet, much as you would with any spreadsheetbased program. Or, if you are currently using another program for metaanalysis, you can either copy data directly from that program or import it using a Wizard.
What if I have multiple subgroups or outcomes within studies?
The program allows you to work with studies that report data for more than one subgroup, outcome, timepoint, or comparison. The program makes it easy to enter data for these studies, and offers a number of options for working with them in the analysis.
Compute the treatment effect (or effect size) automatically
In every metaanalysis you start with the published summary data for each study and compute the treatment effect (or effect size). For example, if a study reports the number of events in each group you might compute the odds ratio. Or, if a study reports means and standard deviations you might compute the standardized mean difference. This process of computing effect sizes is typically tedious and time consuming. In some cases, especially when studies present data in different formats, the process is also difficult and prone to error.
With CMA the process is fast and accurate
With CMA you enter whatever summary data was reported in the published study, and the program computes the effect size from that summary data. For example, you could enter events and sample size, and the program would compute the odds ratio. Or, you could enter means and standard deviations, and the program would compute the standardized mean difference. Three examples (selected from more than a hundred options) are shown here.
What if my data is in some other format?
What if your studies reported data in some other format? Perhaps you have studies that reported only a pvalue and sample size. Or, you have studies that reported an odds ratio and confidence limits. With any other program you would need to compute the effect size and variance for each study before proceeding to the analysis. By contrast, CMA allows you to enter almost any kind of data – it includes 100 formats for data entry similar to the three shown above. Simply locate your data type in a list and CMA will create the corresponding columns in the spreadsheet.
What formula is the program using to compute these effects?
To see the formula used to compute an effect size, doubleclick on that effect size. The program opens a dialog box that shows the exact formula used and also all details of the computation for that specific row.
What if I want to use another index of treatment effect?
In one of the examples shown above we entered events and sample size and the program computed the odds ratio and risk ratio. What if you would prefer to work with the risk ratio? Or what if you wanted to compute the standardized mean difference corresponding to the odds ratio? In another example we entered means and standard deviations and the program computed the standardized mean difference. What if you would prefer to work with the raw mean difference, or to compute the correlation corresponding to the standardized mean difference?
CMA allows you to work with the index of your choice, and to switch back and forth among indices.
For example, if you have entered the events and sample size, the program will compute the odds ratio, log odds ratio, risk ratio, log risk ratio, risk difference, standardized mean difference (d), biascorrected standardized mean difference (g), correlation, and Fisher’s z. Or, if you enter means and standard deviations the program will compute the raw mean difference, standardized mean difference (d), biascorrected standardized mean difference (g), correlation, Fisher’s z, log odds ratio, and odds ratio.
These examples are a subset of the supported formats and indices.
What if different studies reported different kinds of data?
Above, we showed that you can customize the data entry screen to accept almost any kind of data. But what different studies provide different kinds of data? For example, what if one study reported events and sample size while another reported the odds ratio and confidence interval? How would you get both kinds of data into the program?
CMA allows you to mix and match the different data formats. You can enter events and sample size for the first few studies, then odds ratio and confidence interval for the next few studies, log odds ratios with variances for others, and so on. Or, you can enter means and standard deviations for some studies, pvalues for other studies, tvalues for others, and so on. You can customize the spreadsheet with as many kinds of data formats as you like. The program will compute the effect size from each of them and (to the extent possible) allow you to include them all in the same analysis. CMA is the only program to offer this feature.
What if some (or all) of my studies include prepost or crossover designs?
CMA includes templates for more than 20 prepost or crossover designs, which is of particular import since the standard error for these may be difficult to compute otherwise. And, you can mix and match these studies with studies that used posttests alone.
What if I have already computed the effect size?
If you have already computed the effect size and its variance (or standard error) you may enter these directly (the same as you would enter data in any other format).
Can I mix binary, continuous, and correlational data?
As explained above, the program allows you to enter summary data in more than one format – for example, events and sample size for one study and odds ratios with confidence intervals for another. But in this example both studies used binary data. What if some studies report binary data (events and sample size) while others report continuous data (means and standard deviations) or correlational data?
The program is able to convert across these different classes of data. It will convert among odds ratios, standardized mean difference, and correlations so that all may be used in the same analysis.
What if I have studies that look at point estimates rather than effect sizes or treatment effects?
While most metaanalyses work with effect sizes (which assess the relationship between two variables) some are used to estimate a risk, rate, or mean in one group (for example, “What is the risk of Lyme disease?”). CMA will work with these effects (or point estimates) as well.
Can I run a metaanalysis on regression weights?
Yes. In addition to being able to work with recognized effects (such as odds ratios and mean differences) the program is able to work with generic point estimates which may be analyzed either in their original scale or on a log scale.
Perform the metaanalysis quickly and accurately
One click runs the core metaanalysis and creates a display that serves as a roadmap for all that follows.
This display is an interactive forest plot that yields a clear sense of the data  How many studies are included in the analysis, how precise is each of the studies, whether the effect is consistent from study to study or varies substantially across studies, and so on. You can then customize this display as needed. Add or remove columns, set computational options, open tables with additional statistics. Some examples follow.
Display study weights
With one click you can include a column that shows the relative weight assigned to each study. With this mechanism, it becomes clear if the combined effect is a function of many studies, or if it was driven primarily by a small subset of the studies.
Select the computational model
Click on a tab to select the fixed effect model or the random effects model. You can also display the two simultaneously, which makes it possible to see how the point estimate and confidence interval differ between the two models.
Understand how the computational model affects the study weights
The program will also display the relative weights for a fixed effect analysis and a random effects analysis sidebyside. This helps to explain why the combined effect shifts as we move from fixed effect to a random effects model.
Customize the analysis screen
You have full control over the statistics displayed for each study. You can display basic statistics such as the effect size, standard error, and confidence limits. You can display counts, such as events and sample size for each group. You can display diagnostics for each study, such as the residual (the distance from the study to the combined effect).
Select the index of effect size
The tool bar includes a dropdown box that lists all available indices for the treatment effect (or effect size). When you select an effect size such as the odds ratio or standardized mean difference, all statistics, weights, and graphs, are updated automatically.
Display all details of the computations
All computations are displayed on a spreadsheet. You can view this spreadsheet and actually follow all details of the computation. If you are using your own spreadsheet for metaanalysis you can compare this spreadsheet with your own. This also serves as a unique teaching tool.
Create highresolution forest plots with a single click
A key element in any metaanalysis is the forest plot – a plot that shows the effect size and precision for each study as well as the combined effect. This plot puts a face on the analysis – it shows whether the combined effect is based on a few studies or many, whether the effect size is consistent or varies, and so on. As such, the forest plot plays a central role in helping the researcher to understand the data, and also to convey the findings to others.
Most other metaanalysis programs use graphics engines that were developed for other purposes and push them into service for creating forest plots. By contrast, the plotting engine in CMA was developed specifically for the purpose of metaanalysis. It is very easy to use and provides a wide range of important options.
Create a highresolution plot in one click and then customize any element on the plot. Select a symbol for studies, for subgroups, and for overall effect. Optionally, specify that symbols should be proportional in size to study weights, so the studies that contribute the most to the combined effect are easy to spot. Set colors and fonts for each element on the graph, and then export to Word™ or PowerPoint™ in a single click!
Export plots to PowerPoint™
With one click you can open PowerPoint™ and insert a copy of the current slide. The whole process takes about 2 seconds.
Use cumulative metaanalysis to see how the evidence has shifted over time
A cumulative metaanalysis is actually a series of metaanalyses, where each analysis in the sequence incorporates one additional study. For example, the first row in the analysis might include a study published in 1990, the next row would include studies published in 1990 and 1991, and so on. A cumulative metaanalysis may be done retrospectively, to show how the body of evidence has shifted over time (see the Lau study, for example), or prospectively, with new studies being added to the body of evidence as they are completed (see the Childbirth example).
While cumulative metaanalysis is most often used to track evidence over time, it can also be used to show how the evidence shifts as a function of other factors. For example, we could sort the data by study size and run a cumulative analysis. In this case the program would show the combined effect with only the largest studies included (toward the top) and how this effect shifted as smaller studies were added to the analysis (see the passive smoking example). Similarly, we could start with the higher quality studies and see how the effect shifts as other studies are added.
Use a “RemoveOne” analysis to gauge each study’s impact
As part of a sensitivity analysis we might want to assess the impact of each study on the combined effect. For example, what was the impact on the combined effect of an outlier or of an especially large study? Or, did a small study have any impact at all?
To address these kinds of questions the program will automatically run the analysis with all studies except the first, then all studies except the second, and so on. The resulting plot shows the impact of each study at a glance.
Additionally, you have the option of running the analysis with any study or set of studies removed – these can be selected by name, or by the value of a moderator variable.
When running the analysis you can select by (or filter by) any variable or combinations of variables. You could include or exclude studies by study name. You could include studies that had been rated “Yes” for “Doubleblind”. You could include studies where the age had been coded as “Elderly” and the patient type as “Chronic.
Work with multiple subgroups or outcomes within studies
The program allows you to enter data for more than one subgroup, outcome, timepoint, or comparison within studies, and offers various options for dealing with these in the analysis. These options are explained in a whitepaper which you can download here.
Assess the impact of moderator variables
When the effect size varies substantially from study to study an important goal of the meta analysis could be to understand the reason for this variation.
Use analysis of variance to assess the impact of categorical moderators. For example, “Is the treatment more effective for acute patients than for chronic patients?” or “Is homework a more effective intervention than tutoring?”
Use metaregression to assess the impact of continuous moderator variables. For example, “Does the treatment effect increase as a function of dosage?”, or “Is the magnitude of the effect size related to the age of the students?”
Assess the potential impact of publication bias
Metaanalysis provides a mathematically accurate synthesis of available data, but there may be concern that significant studies were more likely to be published than nonsignificant studies, and therefore the pool of available data may be biased. The program includes a set of functions that can be used to assess the potential impact of this bias, as a kind of sensitivity analysis.
系統需求
Platform

XP, Vista, Windows 7, Windows 8
32bit or 64bit

Screen

XGA or higher

Memory

Not relevant.

Disk Space

25 MB

Features

Lite

Std

Pro

Work with a spreadsheet interface

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Work with odds ratios, risk ratios, risk differences, mean differences, standardized mean differences, and correlations

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Enter data for each study in its own format

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More than 30 data formats included

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Compute the treatment effect (or effect size) automatically

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Use ANOVA to assess the impact of moderator variables

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Create highresolution forest plots in Black and White and export to Word

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Create highresolution forest plot in color for export to PowerPoint


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Work also with hazard ratios, ratios of events by person years, risks in one group, mean in one group, generic effects sizes, and more



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Enter data for each study in its own format  more than 100 formats included



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Use metaregression to assess the impact of continuous moderator variables



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Use various mechanisms to assess the impact of publication bias



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Work with multiple independent subgroups within studies



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Work with multiple outcomes, timepoints, or comparisons within studies



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New in V3


Use metaregression to assess the impact of continuous moderator variables, of continuous and/or categorical moderator variables, and interactions



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Full support for Windows 7, Windows 8

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Includes license for computing effect size for multilevel studies



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