similar to: (Meta-analysis) How to build|fake a [n]lm[e] object ?

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

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

Version 2.0-0 of meta (an R package for meta-analysis) is now available on CRAN. Changes are described below. Yours, Guido Major revision R package meta linked to R package metafor by Wolfgang Viechtbauer to provide additional statistical methods, e.g. meta-regression and other estimates for tau-squared (REML, ...) New functions: - metareg (meta-regression) - metabias

Version 2.0-0 of meta (an R package for meta-analysis) is now available on CRAN. Changes are described below. Yours, Guido Major revision R package meta linked to R package metafor by Wolfgang Viechtbauer to provide additional statistical methods, e.g. meta-regression and other estimates for tau-squared (REML, ...) New functions: - metareg (meta-regression) - metabias

Hello again, I m trying to use timereg package as you suggested (R2.7.1 on XP Pro). here is my script based on the example from timereg for a fine & gray model in which relt = time to event, rels = status 0/1/2 2=competing, 1=event of interest, 0=censored random = covariate I want to test library(timereg) rel<-read.csv("relapse2.csv", header = TRUE, sep = ",",

X@n`ax --V@1ium--Phe^ntermine, S0ma right to your door. m.eds from u-s_a & 0v_ernig'ht sh,ipping http://k.l.123a2zrx.us It would be a out and out day-dreaming if you don't want to lose weight via exercise? Now Maridia is with you,you can be a day-dreamer. Again the scene changed, and within a dingy, underground room, hemmed in by walls of stone, and dimly lighted by a

This code worked fine for me, then did some cleaning up of formatting using ESS (Emacs) and now I get this error, no idea what is causing it, all the brackets/parentheses seem to be balanced. What have I done wrong? Thanks Jim p0.trial01 <- 0.25 TruOR01 <- 0.80 num.patients.01 <- 50 num.trials.01 <- 5 LOR01.het.in <- 0.00 num.sims <- 1 simLOR01 <-

Hi, using polynomial contrasts for the ordered factors in an experiment leads to much nicer covariance structure than using treatment contrasts. It is easy to assess the mean effect for each of the experimental groups. However, standard errors are provided only for the components of the orthogonal contrasts. I wonder how to assess the standard errors not of the components, but of the respective

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 ?).

hi Jay, Consult a local statistician. Statistics is not you think is (namely simple computations, R and probably plotting..). regards, vito Jay Zola <jayjay.1988 at hotmail.nl> ha scritto: > Hello, > > > I am medical student, writing a meta-analysis on complication and > reoperation rates after the five most common treatments of distal > radius fractures. I have

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

I have run a glm with a final formula of : (dependent variable = parasite load, main effects are sex, month, length and weight, with sex:month and length:weight first order interactions). I am using the summary(mod) command to give me the contrasts, which I believe use the contr.treatment command. I do not have a treatment group as such as I am comparing data from a wild system so I use the

Hello, for my meta-analysis I try to test if two varainces are equal without using the raw scores. I have is the SD's, N's and the Means. I want to test the variances from dependent and independend samples. I assume I can use the var.test procedure for the independent samples, but what about the dependent samples ? Has anyone an idea how to realise this with R ? Thanks in advance

Hello everybody! I use R 1.6.2 in Windows, and have a problem controlling the na.action. In a dataset with twelve trials, one of the trials lack any readings of the variable "STS.SH" (standing power at harvest) Fitting an aov() object with the call: led1t7sts.aov <- aov(STS.SH ~ Trial/Block + Treatment + Treatment:Trial, data = led1t7, na.action=na.exclude) seems to work as it

Hello, I am medical student, writing a meta-analysis on complication and reoperation rates after the five most common treatments of distal radius fractures. I have been busy with the statistics for months by my self, but find it quite hard since our classes were very basic. Now I want to compare the treatment modalities to see if there are significant differences. Using R I was able to

Dear Vito, Thank you for your reply. I tried to contact the statistics departement numerous times, but did not receive any reply. That is why I started to look on the internet for help. Yours sincerely, Jay Verstuurd vanaf mijn iPhone > Op 26 jun. 2017 om 22:05 heeft Vito Michele Rosario Muggeo <vito.muggeo at unipa.it> het volgende geschreven: > > hi Jay, > Consult a local

Hello, I was hoping that someone could answer a few questions for me (the background is given below): 1) Can the coxph accept an interaction between a covariate and a frailty term 2) If so, is it possible to a) test the model in which the covariate and the frailty appear as main terms using the penalized likelihood (for gaussian/t frailties) b)augment model 1) by stratifying on the variable that

Hi, I am new in statistics and i manage to make the linear model analysis but i have some difficulties in explaining the results. Can someone help me explalinig the output of my linear model analysis ? My data are with 2 variables habitat (e,s) and treatment (a,c,p) with multiple trials within. Thank you in advance Call: lm(formula = a$wild ~ a$habitat/a$treatment/a$trial) Residuals: Min

Hi I'm encountering some problems with coxme My data: I'm looking at the survival of animals in an experiment with 3 treatments, which came from 4 different populations, two of which were infected with a parasite and two of which were not. I'm interested if infected animals differe from uninfected ones across treatments. Factor 1: treatment (3 levels) Factor 2: infection state

Hi everyone, Although this question is more related to ChIP and ChIP-seq, it could be probably anchored in a more general statistical context. The question is : what method is better to assess the significance of the change in a signal (the signal can be DNA binding, for instance) given the background and 2 conditions. <. condition1 (eg no treatment) : background = 1;