similar to: LME as part of meta-analysis

Dear Wolfgang Viechtbauer and list members: I have discovered your "MiMa" function for fitting meta-analytic mixed-effects models through an earlier discussion on this list. I think it is extremely useful and fills an important gap. In particular, since it is programmed so transparently, it is easy to adapt it for one's own needs. (For example, I have found it easy to identify

I would like to combine time-series data to test for correlations and interactions using random and fixed effects meta-analysis. So, I am looking for the right packages and documentation. I know about meta and rmeta packages of R. Are there any more? What are the diffrences in brief? Can you please suggest some references that could be used as a guide for meta-analysis in R (or S-plus)?

Dear List, I am (again) looking at meta-regression as a way to refine meta-analytic results. What I want to do is to assess the impact of some fixed factors on the results of a meta-analysis. Some of them may be crossed with the main factor of the meta-analysis (e. g. clinical presentation of a disease, defining subgroups in each of the studies under analysis), some of them may be a grouping

Dear colleagues, I'm conducting a meta-analysis of studies evaluating adherence of HIV-positive drug users into AIDS treatment, therefore I'm looking for some advice and syntax suggestion for running the meta-regression using proportions, not the usual OR/RR frequently used on RCT studies. Have already searched already several handbooks, R-manuals, mailing lists, professors, but... not

Hi, I would like to do a meta-analysis, i.e., a mixed-effects regression, but I don't seem to get what I want using both the nlme or metafor packages. My question: is there indeed no way to do it? And if so, is there another package I could use? Here are the details: In my meta-analysis I'm comparing different studies that report a measure at time zero and after a certain followup

Hi I'm conducing a meta-analysis using the meta package. Here's a bit of code that works fine - tmp <- metacont(samplesize.2, pctdropout.2, sddropout.2, samplesize.1, pctdropout.1, sddropout.1, data=Dataset, sm="WMD") I would now like to control for a couple of variables (continuous and categorical) that aren't in the equation. Is meta

I had found the author's (Wolfgang Viechtbauer) earlier meta-analytic code in R, MiMa, useful. so I have been exploring metafor using an example dataset from MiMa. metafor provides a lot more. However, MiMa provided parameter estimates, standard errors, z values, etc. for individual moderators in the meta-analysis, but I don't see how to obtain these from metafor. Have you any help

R-experts: In 2002, Hans Van Houwelingen et al. published a tutorial on how to do meta-regression in Statistics in Medicine. They used the classic BCG dataset of Colditz to demonstrate correct methodology and computed the results using PROC MIXED in SAS. In trying to duplicate the results presented in this paper, I have discovered that I can reproduce certain items with lmer but not

I have little idea what actions this relates to, but I see about 200 or more of these errors each day in the messages log file: May 29 15:55:41 mima kernel: CIFS VFS: strtoUCS: char2uni of -127 returned -22 No one has complained of a problem. Does anyone know what it signifies? --Donald

I have a meta-analysis dataset which I would like to analyze as a mixed model, where the y-variable is a measure of effect size, the random effect is the study from which the effect size was extracted, and the fixed effect is a categorical explanatory variable. The complication is that we often have multiple estimates of effect size from a single study (e.g. the experiment was repeated in

Dear R-community, I'm struggling with a paper that reports only fragmented results of a 2by2by3 experimental design. However, Means and SDs for all cells are given. Does anyone know a package/function that helps computing an ANOVA with only Means and SDs as input (resulting in some sort of effect size and significance test)? The reason why I'm interested is simple: I'm conducting a

Dear list-members, I am trying to use R to conduct a meta-analysis, i.e. I'd like to use a multi-level model to integrate the findings of a number of primary research studies. I set up a simple two level-model (only summary statistics are provided by each study) as follows: sapp.lme <- lme(D ~ 1, data = sapp.frame, random = ~ 1 | STUDYNR, weights=varFixed(~-1+STDERR_D),na.action =

Dear all, We are trying to do a meta regression in R using the lme function. The reason for doing this with lme function is that we have covariates and studies within references. In S-Plus this is possible by using the following command: lme(outcome ~ covars, random = ~1 | reference/study, weights = varFixed(~var.outcome), data = mydata, control = lmeControl(sigma = 1)) This means that the

I have the following vector of strings (shown only the first 3 elements) > desc[1:3] [1] "hsa-let-7a MIMAT0000062 Homo sapiens let-7a" [2] "hsa-let-7a* MIMAT0004481 Homo sapiens let-7a*" [3] "hsa-let-7a-2* MIMAT0010195 Homo sapiens let-7a-2*" > is.vector(desc) [1] TRUE > A <- unlist(strsplit(desc[1:3], " ")) > A [1]

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

Hi, I have developed a logistic regression model in the form of (factor_1~ numeric + factor_2) and would like to perform a cross-validation or some similar form of sensitivity analysis on this model. using cv.glm() from the boot package: # dataframe from which model was built in 'z' # model is called 'm_geo.lrm' # as suggested in the man page for a binomial model: cost <-

I'm looking for someone to explain the difference between these procedures. The function prcomp() does principal components anaylsis, and the function cmdscale() does classical multi-dimensional scaling (also called principal coordinates analysis). My confusion stems from the fact that they give very similar results: my.d <- matrix(rnorm(50), ncol=5) rownames(my.d) <-

Hello, I have some data where a number of events (the total amount varies) occur at cumulating times, I would like to create a scatterplot (easily achieved using plot etc) of these events (the events can either be times using poxist or I can convert them into just seconds which is probably easier to work with), however I would like the events/times to re-begin plotting every 10th occurrence and

Anyone have a good R tutorial for time series? Or know of a good book? Any advice would be helpful. I've been using matlab and programming things directly (not using canned functions), but now I'm having difficulty figuring out how to say, do freq domain anaylsis and get a CI on my periodogram, or plotting the coherence, etc. Or how to change certain parameters, e.g. fit AR(p) with p

*** If anyone has a better way of doing this, please post to the list. I hadn't seen anything on this list or in channel.c/chan_local.c - which prompted this email *** I'm not sure how many VoIP providers out there are using Asterisk as a service platform like we do, but I thought I'd share an experience with call looping that was racking my brain with the list. One of the