similar to: Randomization for block random clinical trials

Hello R people, I am looking to pay someone to help write some R code. Inputs: Study identifier: ID Number for the study, each ID number is for one study only each block set should only be used for that study. This will require that you store the results from the blocks someplace on the file system. Trait #1: dichotomous rural / urban Trait #2: dichotomous sick / healthy Assignment Ratio:

Can anyone recommend a package that can be used to randomize subjects? I am looking for a generalized package, or several packages that can accomplish unrestricted randomization (i.e. simple random assignment) restricted randomization including stratified randomization, blocked randomization, and adaptive randomization. Thanks, John John David Sorkin M.D., Ph.D. Chief, Biostatistics and

Hi all, I've been messing with this for a while now.. myStudy is a matrix of word frequency to date of document/document. As you can see I'm trying to plot the series in the matrix. I'd like three things -- 1- In the plot that gets created the first series is a solid line, the rest are dashed... how do I make them either one or the other. 2- The legend that comes up has filled boxes

Hi, I am very new to R. Just started yesterday. I have to generate a sequence of 40 random treatments using permuted block randomization with a block size of 8. Then I have to plot moving averages for the resulting sequence. I have tried the sample function but I dont know if wht i did is right or not > sample(c(1,1,1,1,2,2,2,2),8) Any ideas please? -- Ayesha Khan MS Bioinformatics Dept. of

This is to announce an updated version of the blockrand package has been uploaded to CRAN. The blockrand package is used for creating randomizations for clinical trials or other studies where random assignments are made one at a time. It implements the ideas of randomizing within blocks so that the numbers of subjects in each group remain fairly balanced (but allows for unbalanced blocks to make

This is to announce an updated version of the blockrand package has been uploaded to CRAN. The blockrand package is used for creating randomizations for clinical trials or other studies where random assignments are made one at a time. It implements the ideas of randomizing within blocks so that the numbers of subjects in each group remain fairly balanced (but allows for unbalanced blocks to make

I used both BMDP and SAS in my earlier years, side by side. At that time the BMDP statistical methods were much more mature and comprehensive: we treated them as the standard when the two packages disagreed. (It was a BMDP manual that clearly explained to me what the hypothesis of "Yate's weighted mean test" is, something SAS decided to call "type III" and eternally

Dear All, I am looking an R version of the "Computer program for sample size and power calculations in the design of multi-arm and factorial clinical trials with survival endpoints". Best regards, Giovanni Parrinello P.S.: in the meantime I am preparing a summary for my preceeding question about time-varying covariates in the Cox model and I thank Frank Harrell, Chuck Cleland,

Dear R-Helpers, Apologies in advance as this is partly (widely ?) OT. Not sure where to ask, R is my favorite computer tool (no kidding) and there are plenty of knowledgable and helpful people on the list. Background: There are discussions among experts and regulatory authorities (cf guideline http://www.emea.europa.eu/pdfs/human/ewp/056598en.pdf) that, in for example Amyotrophic Lateral

The package blockrand is now available on CRAN. This package aids in doing block randomizations for clinical trials or other situations where subjects or experimental units arrive over time and need to be randomized one at a time. The block randomization keeps the number of subjects in each arm of the trial fairly balanced. The package also includes a plot function that creates a pdf file of

I hope that Marc doesn't mind, but I felt that part of his recent post was important enough to deserve it's own subject line rather then being lost in a 60-msg-long thread... On Sun, Jan 11, 2009 at 10:08 AM, Marc Schwartz <marc_schwartz at comcast.net> wrote: ... I strongly believe that the comments regarding R and the FDA are overly negative and pessimistic. The hurdles to

Hello Everyone,   I’m a SAS user who has recently become interested in sequential clinical trials designs. I’ve discovered that the SAS based approaches for these designs are either too costly or are “experimental.” So now I’m looking for alternative software. Two programs that seem promising are SPLUS Seqtrial and R.   I recently obtained a 30 day trial for the SPLUS Seqtrial add-on and have

I am working on a new package, one in which the user needs to specify the role that different variables play in the analysis. Where I'm stumped is the best way to have users specify those roles. Approach #1: Separate formula for each special component First I thought to have users specify each formula separately, like: new.function(formula=y~X1+X2+X3, weights=~w,

Hello, I am using Dr. Harrell's design package to make a nomogram. I was able to make a beautiful one without stratifying, however, I will need to stratify to meet PH assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram:

Dear R users, I have a large individual-level dataset (~700,000 records) which I am performing a conditional logistic regression on. Key variables include the dichotomous outcome, dichotomous exposure, and the stratum to which each person belongs. Using this individual-level dataset I can successfully use clogit to create the model I want. However reading this large .csv file into R and running

>Hello, I am hoping for assistance in regards to examining the contribution of stratified variables in a cox regression. A previous post by Terry Therneau noted that "That is the point of a strata; you are declaring a variable to NOT be proportional hazards, and thus there is no single "hazard ratio" that describes it". Given this purpose of stratification, in the

Dear WizaRds, Working through sampling theory, I tried to comprehend the concept of stratification and apply it with Survey to a small example. My question is more of theoretic nature, so I apologize if this does not fully fit this board's intention, but I have come to a complete stop in my efforts and need an expert to help me along. Please help: age<-matrix(c(rep(1,5), rep(2,3),

Good afternoon! I'm trying to use the Survey package for a stratified sample which has 4 criteria on which the stratification is based. I would like to get the corrected weights and for every element i get a weight of 1 E.g: tipping design <- svydesign (id=~1, strata= ~regiune + size_loc + age_rec_hhh + size_hh, data= tabel) and then weights(design) gives

Dear WizaRds, having met some of you in person in Vienna, I think even more fondly of this community and hope to continue on this route. It was great talking with you and learning from you. Thank you. I am trying to work through an artificial example in post stratification. This is my dataset: library(survey) age <- data.frame(id=1:8, stratum=rep(

Dear all, There have been a variety of discussions on the R list regarding the use of R in clinical trials. The following post from the STATA list provides an interesting opinion regarding why SAS remains so popular in this arena: http://www.stata.com/statalist/archive/2008-01/msg00098.html Regards, -Cody Hamilton