similar to: Newbie problem ... Forest plot

Der useRs, I'm working on meta analysis using rmeta package. Using code below I plot the forest plot: library(rmeta) data (catheter) a<-meta.MH (n.trt, n.ctrl, col.trt, col.ctrl, data=catheter, names=Name, subset=c(13,6,5,3,7,12,4,11,1,8,10,2)) summary(a) # odds ratio values and confidence intervals metaplot(a$logOR, a$selogOR, nn=a$selogOR^-2,a$names, summn=a$logMH, sumse=a$selogMH,

Dear Sir or Madam, I am trying to plot forest plot. I extracted odds ratio and their corresponding 95% confidence interval from papers, then I calculated the log(OR) and standard error using the following command OR<-metagen(logOR,selogOR,sm="OR") forest(OR,comb.fixed=TRUE,comb.random=TRUE,digits=2) However, it does not produce a forest plot. Can someone kindly help? Thank

Dear R users: In the example of meta-analysis (cochrane, package rmeta), I can not found the p-value of Test for overall effect, and some other indices (Z, I, weight and et al). How can I get the these indices listed? > library(rmeta) > data(cochrane) > cochrane name ev.trt n.trt ev.ctrl n.ctrl 1 Auckland 36 532 60 538 2 Block 1 69 5

Hello, I'm ashamed to admit that I'm an R and meta-analysis neophyte just when my colleagues and I are attempting to run R for that latter purpose. We would like to construct a graph of the 95% confidence intervals of odds ratios (inverse odds ratios, actually). We are using the metaplot() function in the "rmeta" package. While we can construct a basic plot, we cannot configure

Dear R-list, I'd like to do a meta-analysis plot similar to install.packages('rmeta') require(rmeta) data(catheter) a <- meta.MH(n.trt, n.ctrl, col.trt, col.ctrl, data=catheter, names=Name, subset=c(13,6,5,3,7,12,4,11,1,8,10,2)) summary(a) plot(a) (see attached file) by using my own OR (Odds Ratio) and 95% Confidence Interval data set, which looks like

Dear R-Community, I'm currently trying to find a way to conduct a meta-analysis in R. I would like to analyze data from mostly-cross-sectional survey-studies. The effect sizes would be correlations. The R packages "meta" and "rmeta" are, as far as I can see, set up for analysis with effect sizes for differences (i.e. comparison of the means/odds-ratios of experimental

Hello, After looking through the archive and documentation for quite some time, I'd be very happy about some help with metaplot and meta.summaries. metaplot: --------- Can I change the label size? I've got 126 values and the intersection of the labels makes it impossible to read them. Why do I have to give sumse (Standard Error) and sumnn (Precision) of the summary estimate? I can

Hi, can you tel me how can I make a Forrest Plot with R? It is possible and easy or are there a more practical free software available? Than you Mic [[alternative HTML version deleted]]

Does anyone know which package include the computation of Cochran’s Q statistic in R? jlfmssm [[alternative HTML version deleted]]

I tried to post this a few times last week and it seems to have got stuck somehow so I'm trying from a different email in the hope that works. If somehow this has appeared on the list 20 tiems and I never saw any of them I apologize ;-) I'm basically an R-newbie. But I am VERY computer literate. But this has me stumped... All the examples for using the rmeta package to create a

I have to study censured datas concernig the occurence of infection at the point of insertion of catheter in patients with renal disease. Catheter may be removed for ather reasons than infection, in this case, the observation is censored. Each has exactly 2 observations. The question are the pronostic factors of infection (the other variables are; age, sex, type of renal disease...). Could you

Dear all, I could not draw a forest plot for meta-analysis under ramdon models using the rmeta package. The rmeta has a default function for MH (fixed-effect) model. Has the rmeta package been updated for such a function? Or someone revised it and kept a private code? I would appreciate it if you could provide some information on this question. Thanks, Andrew This email is intended

Hello, I'm using the rmeta package to perform a meta analysis using summary statistics rather than raw data, and would like to analyze the effects in three different subgroups of my data. Furthermore, I'd like to plot this on one forest plot, with corresponding summary weighted averages of the effects displayed beneath each subgroup. I am able to generate the subgroup analyses by simply

Hello I'm trying to plot hazard risk values using the function metaplot with the specifications: > metaplot(HR,SE,W,labels=row.names(lc),xlab="Hazard Ratio",ylab="Covariates", logeffect=TRUE,logticks=FALSE,colors=meta.colors(box="black",lines="dark gray",zero="darkgray"),cex=1.5,cex.lab=1.5,font=3) However, in the plot the x axis

Hello, I have a data with following format:      Predictors          n11     n12     n21     n22     Odds.Ratio    log.ratio    se.log.odds. 1 ProcOR respirato     2 ProcVaric vein      3 DiagCardiac anom   4 DiagAllergy        5  DiagOth skin dx    6    DiagGastritis          I want to plot odds ratio by command: forestplot in rmeta library, but I get the following error constantly.  Error in

Dear all, my apologies if I am posting a silly question - but I couldn't find any helpful information on this elswhere. In the R FAQ, version 1.8-1, there is a section "5.2 How can add-on packages be installed?", but it only gives information for Unix users. How can I install an add-on package under Windows? (Specifically, I have downloaded the "rmeta" package from CRAN.)

Dear R-lister, is there any function for Meta Analysis in R? (like homogeneity an, risk differences, relative riskm amd odds ratios? Many thanks, Edwin

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

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)?

Hello everyone, I want to do a meta-analysis of case-control studies on which an OR was computed based on a continuous exposure. I have found several several packages (metafor, rmeta, meta) but unless I misunderstood their main functions, it seems to me that they focus on two-group comparisons (binary independent variable), and do not have the option of using a continuous independent variable.