I guess I'll chip in, since I wrote that function (which is going to be updated thoroughly in the near future -- I will probably expand it to an entire package).
> > Have a look at "MiMa" at Wolfgang Viechtbauer's page. Is that what > > you are looking for? > > > > <http://www.wvbauer.com/downloads.html> > > As far as I can tell, mima does what I mean to do, but there are some > limits : > > - mima works on effects, and therefore has an "unusual" form in R models The dependent variable to be used with the mima function can be any measure for which we have a known sampling variance (or approximately so) and that is (approximately) normally distributed. So, the dependent variable could be log odds ratios, log risk ratios, standardized mean differences, and so on. Are you looking for the option to input the results from each study arm individually? (e.g., the log odds for a control and a treatment group). You could also use mima then (with an appropriately coded moderator). However, it would then make more sense to assume a common (but random) intercept for all the arms from a single study. At this point, the function isn't set up that way, but I think I could rewrite it to do that. > - as far as I can tell, mima allows to asses the effect of variables > *nesting* studies, but not of variables *crossed* in each study ; > therefore, ypou cannot directly test the effect of such variables ; I am not sure if I understand this point. I think this may relate to the fact that (if I understand it correctly), you want to input the results from each arm separately. > - as far as I can tell, the variables of interest ("moderators", in mima > parlance) can be either two-level factors, booleans or numeric > variables, i. e variables having a single regression coeffiient : mima > builds an estimator for the regression coefficient of each variable and > its variance, and tests by a Z-test. This is not applicable to n-valued > factors (n>2) or ordered factors, which could be tested by > {variance|deviance} analysis. You can also test for blocks of moderators with the mima function. Let's say you have two dummy variables that are coded to indicate differences between three groups (e.g., low, medium, and high quality studies). Now you want to test if quality makes at all a difference (as opposed to testing the two dummy variables individually). Use the out="yes" option and then do the following: 1) from $b, take the (2x1) subset of the parameter estimates corresponding to the two dummy variables; denote this vector with b.sub 2) from $vb, take the (2x2) subset from the variance-covariance matrix corresponding to the two dummy variables (i.e., their variances and the covariance); denote this vector with vb.sub 3) then t(b.sub) %*% solve(vb.sub) %*% b.sub is approximately chi-square distributed under H0 with 2 degrees of freedom. I am also going to add to the function the option to output the log likelihood value. Then likelihood ratio tests are a snap to do with full versus reduced models. But for now, the above should work. Feel free to get in touch with me via e-mail. Best, -- Wolfgang Viechtbauer Department of Methodology and Statistics University of Maastricht, The Netherlands http://www.wvbauer.com/ ______________________________________________ [email protected] 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.
