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【求助】新手求助！mean+SEM和mean+SD之间，要做meta分析怎么转化？
【求助】新手求助！mean+SEM和mean+SD之间，要做meta分析怎么转化？
分享到哪里？
meta菜鸟新手求助各位老师，想做一篇连续性变量的meta分析，但同一个指标有的文献是用mean+SEM，有的文献是用mean+SD，请问我后面统计学合并计算的时候怎么转化这两个指标呢？
【求助】新手求助！mean+SEM和mean+SD之间，要做meta分析怎么转化？
分享到哪里？
虽然有转化的文章，但是cochrane手册不推荐
回复：【求助】新手求助！mean+SEM和mean+SD之间，要做meta分析怎么转化？
分享到哪里？
afiyyn 虽然有转化的文章，但是cochrane手册不推荐 那请问我后面的统计可以进行么？怎么进行呢？非常感谢！！！
回复：【求助】新手求助！mean+SEM和mean+SD之间，要做meta分析怎么转化？
分享到哪里？
文献《Estimating the mean and variance from the median, range, and the size of a sample》2005年发表的，附件下载。我觉得当你的样本量小于70个的话就别转了，转换后求得的异质性太强而且cochrane手册不推荐，关于这个问题，论坛之前讨论过，你可以搜索下。
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药理活性中SEM和SD的问题
药理活性中数据表达mean±SEM,& & SEM的范围是多少在可接受范围内?
药理活性中数据表达mean±SD,& && &SD的范围是多少在可接受范围内?
希望能说具体点
mean±SEM与mean±SE表示的意义相同，即均数加减标准误，而mean±SD表示的是均数加减标准差。前者表示定量资料抽样分布的均数的分布情况，而后者表示定量资料（满足或近似满足正态分布）的个体测量值的分布情况，意义完全不同。一般情况下是以均数加减标准差来表示。 SD反映了mean对该组数据的代表性，SEM描述了抽样误差的大小，反映了mean的可靠性。 : Originally posted by zhenwuhuang at
mean±SEM与mean±SE表示的意义相同，即均数加减标准误，而mean±SD表示的是均数加减标准差。前者表示定量资料抽样分布的均数的分布情况，而后者表示定量资料（满足或近似满足正态分布）的个体测量值的分布情况，意 ... SEM数值多大在可接受的误差范围之内New View of Statistics: Mean & SD or SEM?
View of Statistics
· &·
Summarizing Data:
± SD or MEAN ± SEM?
The standard deviation (SD)
represents variation in the values of a variable, whereas the
standard error of the mean (SEM) represents the spread that the mean
of a sample of the values would have if you kept taking samples. So
the SEM gives you an idea of the accuracy of the mean, and the SD
gives you an idea of the variability of single observations. The two
are related: SEM&=&SD/(square root of sample size).
Some people think you should show SEMs with means, because they think it's
important to indicate how accurate the estimate of the mean is. And when you
compare two means, they argue that showing the SEMs gives you an idea of whether
there is a
difference
between the means. All very well, but here's why they're heading down the wrong
For descriptive statistics of your subjects, you need the SD
to give the reader an idea of the
between subjects. Showing an SEM with the mean is silly.
When you compare group means, showing SDs conveys an idea of
the magnitude of the difference between the means, because you can
see how big the difference is relative to the SDs. In other words,
you can see how big the
It's important to visualize the SDs when there are several
groups, because if the SDs differ too much, you may have to use
before you compute confidence limits or p
values. If the number of subjects differs between groups, the SEMs
won't give you a direct visual impression of whether the SDs
If you think it's important to indicate , show
outcome statistic That's more accurate than showing SEMs. Besides,
does anyone know how much SEMs have to overlap or not overlap
before you can say the difference is significant? And does anyone
know that the amount of overlap or non-overlap depends on the
relative sample sizes?
Most importantly, when you have means for pre and post
scores in a
experiment, the
SEMs of these means do NOT give an impression of statistical significance
of the change--a subtle point that challenges many statisticians. So if the
SEMs don't show statistical significance in experiments, what's the point
of having them anywhere else?
Here's a figure to illustrate why SEMs don't convey statistical
significance. It's for imaginary data in an experiment to increase
jump height. The change in height is significant (p=0.03) when the
measurement of jump height has high reliability, but not
significant (p=0.2) when the reliability is low. But the SEMs are
the same in both cases:
The SEMs of the post-pre change scores in a treatment and control group
would indicate statistical significance. But if you show the change
scores, you should show the confidence interval for the change, not the SEM.
You should also show the SD of the change scores for the treatment and control
groups, because a substantial increase in the SD of the change scores in a
treatment group relative to a control group indicates
to the treatment. SEMs of the change scores would alert you
to the possibility of individual responses only if the sample size was the
same in both groups.
So when you see SEMs in a publication, smile, then mentally convert them into
SDs to see how big the differences are between the groups. For example, if there
are 25 subjects in a group, increase the size of the SEM by a factor of 5 (=&square
root of 25) to turn it into an SD.
The bottom line: never show SEMs. Never. Trust me.
Here endeth precision of measurement and summarizing data. On the
we start generalizing to a
population.
· &·
Last updated 25 June 03}