Roy Sanderson a écrit : > Hello > > I'm very much a beginner on meta-analysis, so apologies if this is a > trivial posting. I've been sent a set data from separate experimental > studies, Treatment and Control, but no measure of the variance of effect > sizes, numbers of replicates etc. Instead, for each study, all I have > is the mean value for the treatment and control (but not the SD).
With possibly three very special kind of exceptions, what you've been sent is insufficient for any kind of analysis (meta- or otherwise) : no way to assess variability, hence no way to assess relative importance of noise to data or relative importance of different set of data. One possible exception is when the very nature of the experiment imply that your data come from a truly one-parameter distribution. I'm thinking, for example, of count data of rare events, which might, under not-so-unreasonable(-in-special-circumstances) conditions, come from a Poisson distribution. Another possible set of exception is that when the second (and following) parameter(s) can be deduced from "obvious" general knowledge. For example (set in a semi-imaginary setup), one may give you the number of people using a given service at least once during a specified period, *provided* that in order to use this service, people have to register with the service provider first. The data might be a simple number (no valid indication of variability, if service use is too ferquent to be modeled by a Poisson distribution), but looking up the number of registered users in some data bank might provide you with a valid proportion and population size, which is enough to meta-analyze. But the third possibility is of course that your "means" are indeed the result of experimenting on *ONE* experimental unit of each group. This is highly dependent of what is measured and how (an example of this might be industrial production per unit time with two different set of tools/machines in various industrial setups : here, the experimental unit is the industrial setup, and your "mean" is *one* measure of speed). Then, you have *individual* data, that you should analyze accordingly (e. g. t-test or Wilcoxon test if there is no relationship between "treated" and "control" experimental unit, paired t-test or paired Wilcoxon test if you are told that the "means" may be related, etc ...). This is not a "meta-analysis", but an analysis. Outside these possibilities, I see no point of "meta-analysing" anything that isn't analysable by itself. > As > far as I can tell, this forces me into an unweighted meta-analysis, with > all the caveats and dangers associated with it. As far as I can tell, you're forced to tell your sponsor/tutor/whatever either that he doesn't know what he asks for or that he's trying to fool you (and you saw it !) ; which might lead you to ask him to rethink his question, give you more informatin about the measumements and experimental setup, to provide you (or help you find) the missing data, to stop playing silly games or to go fly a kite... > Two possible approaches > might be: > > a) Take the ln(treatment/control) and perform a Fisher's randomisation > test (and also calculate +/- CI). > b) Regress the treatment vs control values, then randomise (with or > without replacement?) individual values, comparing the true regression > coefficient with the distribution of randomisation regression > coefficients. I haven't the foggiest idea of what you're trying to do here : introducing artficial variability in order to separate it for variation between groups ? Unless you are in the case of "one experiment = one experimental unit per group" (see above) with no information about variability, the only information you can use is the *sign* of the difference "Experimental"-"Control" : if all or "almost all" of them go "in the same direction", one might be tempted to conclude that this direction is not random (that's the sign test) . But this is only valid if the hypothesis "Direction is 50/50 random under H0" has validity under the experimental setup, which your story doesn't tell... > Both approaches would appear to be fraught with risks; for example in > the regression approach, it is probable that the error distribution of > an individual randomised regression might not be normal - would this > then invalidate the whole set of regressions? Again, you'd work against an artificial variability that you'd have introduced yourself : what is the point ? HTH, Emmanuel Charpentier ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.