similar to: Meta-regression with lmer() ? If so, how ?

Hi, I'm having a problem using LME and hopefully I can get some help. Here is what I'm doing: I have a model like this: yi = alpha + theta * xi + beta * zi + error, errors are normally distributed mean 0, var sigma^2 xi and zi are generated from normal distributions within a specified range. True values of alpha, theta, beta and sigma^2 are chosen with a specific mean and variane

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). As far as I can tell, this forces

Dear all, I recently had to review the current litterature about some medical treatment with two possible variants (let's call them A and B). I collected all available prospective randomized trials about this treatment : I got four trials for the A variant and three for the B variant, all studies comparing one variant to a "suitably choosen" placebo. Two classes of variables are of

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

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

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

Dear List, I have had problems inserting some (not all !) figures via odfWeave (using print(someLatticeFunction)...). The figure was correctly displayed in a R device window but the resulting ODF document displayed the correct space for the figure and an empty frame with a "broken image" icon and a "read error" mention. Exploration of the odf (.odt, in my case) file showed

Full_Name: Emmanuel Charpentier Version: 1.6.1 OS: Linux 2.4.20 / Debian Woody Submission from: (NULL) (80.15.78.96) rsprng depends on libraries available in the "genesis" package. However, even when Genesis is installed, the installation of the source package fails for not finding rsprng.h. It turns out that Debian installs it in a subdirectoru of /usr/include, and that the rsprng

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)?

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

Dear All, I used lmer for data with non-normally distributed error and both fixed and random effects. I tried to calculate a "Type III" sums of squares result, by I conducting likelihood ratio tests of the full model against a model reduced by one variable at a time (for each variable separately). These tests gave appropriate degrees of freedom for each of the two fixed effects, but

Dear list, I have to read some clinical data a file coming from Filemaker on Macintosh (Ugh ! But it could be worse and come from Excel...). Exporting via Excel is out of question since the file has 467 columns and 121 lines (+ headers), which is out of reach of Excel. So I received an "mer"" files, which is what Filemaker exports as a text file. It seems to be a semicolon

Dear list, dear Max, I a currently working on a report. I'm writing it with OpenOffice.org and odfWeave. I'm working increentally : I write a bit, test (interactively) some ideas, cutting-and-pasting code to the Ooo report when satisfied with it. I the process, I tend to recompile the .odt source a *lot*. Suddenly, odfWeave started to give me an incomprehensible error even before

Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between

Hello friends of R list, Im a physician and Im not that good in statistics. I have posted similar email in the epi-sig list but no one aswered so far. Im cunducting a systematic review on two diagnostic test for a particular tropical disease. I found a free software that make most of the analysis called MetaDiSc. However there is a paticular analysis that I wuould like to try that it is not

Dear list, I wish to define a set of functions *auxilliary* to another set of "main" ones, and I wonder if there is some "clever" way do do this using lexical scoping. Looking for that in the list's archives did not get me easily understood answers. Perusing MASS (1st, 2nd, 3rd and 4th editions!) and "Programming S" wasn't of much help either... R easily

Hi ! I'm trying to install Dragon Naturally Speaking in a WINE "fake windows" setup, and I have troubles with InstallShield. I tried this a while ago (with wine 20031212) and had *partial* success : The app installed, by I had trouble with the character set settings and botched the voice training, 'cause I couldn't read the screen ... I reinstalled a new machine

Dear all, I want to do a logistic regression. So far I've only found out how, in a dataset of complete cases. I'd like to do logistic regression via max likelihood, using all the study cases (complete and incomplete). Can you help? I'm using glm() with family=binomial(logit). If any covariate in a study case is missing then the study case is dropped, i.e. it is doing a complete case

Dear list members, I try to simulate an incomplete block design in which every participants receives 3 out of 4 possible treatment. The outcome in binary. Assigning a binary outcome to the BIB or PBIB dataset of the package SASmixed gives the appropriate output. With the code below, fixed treatment estimates are not given for each of the 4 possible treatments, instead a kind of summary

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).