similar to: BMDP and SAS (was R in clinical trials)

[Sorry, In case this is repeating a message already sent to the list]. I am trying to move a project to R (base or nlme), for which I have a partial solution in BMDP 8V. Here is the 8V control language: /input title='Augenbewegungen'. variables=4. file='latm.dat'. format='11x,f7.0,f7.0,f12.4,f10.0'. /variables names= llat,rlat,vg,diff. /design

I am trying to translate a given BMDP 8V problem treatment to something approximately equivalent in nlme package (or base R, if sufficient). (Of course, it is not my goal to end there. I want to get access to the advanced flexibility of a more modern treatment.) Here is the problem statement in BMDP control language: /input title='Augenbewegungen'. variables=4.

Hi All, I've been fiddling around with various ways to estimate the popularity of R, SAS, SPSS, Stata, JMP, Minitab, Statistica, Systat, BMDP, S-PLUS, R-PLUS and Revolution R. It's not an easy task. You can see what I've come up with so far at http://r4stats.com/popularity . I'm sure people will have plenty of ideas on how to improve this, so please let me know what you think.

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

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,

Hi, I’m new to R (just installed today) and I’m trying to figure out how to do stratified randomisation using it. My google search expedition has lead me to believe that blockrand package will most probably be the answer to it. I’ve played around with blockrand for awhile and tried the sample code: library(blockrand) ##stratified by sex male <- blockrand(n=100,

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

Scot, Thanks for the info. I will try your code out to verify the result, but before I do that, will your code (SAS and R) work with variable names that are longer than 8 characters long without truncating the variable name in R? Also, I wonder about using your method or the PROC EXPORT method with larger data sets. The data sets I will be working with for the most part will not be that large,

I have design with two repeated-measures factor, and no grouping factor. I can analyze the dataset successfully in other software, including my legacy DOS version BMDP, and R's 'aov' function. I would like to use 'Anova' in 'car' in order to obtain the sphericity tests and the H-F corrected p-values. I do not believe the data are truly deficient in rank. I

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

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

I have a problem moving from multistratum aov analysis to lmer. My dataset has observations of ampl at 4 levels of gapf and 2 levels of bl on 6 subjects levels VP, with 2 replicates wg each, and is balanced. Here is the summary of this set with aov: >> summary(aov(ampl~gapf*bl+Error(VP/(bl*gapf)),hframe2)) > >Error: VP > Df Sum Sq Mean Sq F value Pr(>F) >Residuals

Is anybody using R to do analysis of clinical trial datasets that have been put in the public domain (which are super hard to find). Not only a single data table, but the actual database, with a handful of data tables with one-to-one or many-to-one relationships? [ For example, "Adverse Events" and "Patient Info" are two datasets with a many-to-one relationship, the

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

Dear all, I am happy to announce that {simtrial} is now on CRAN (https://cran.r-project.org/package=simtrial). simtrial is a fast and extensible clinical trial simulation framework for time-to-event endpoints. This release brings a new tabular data processing engine powered by data.table for 3x to 5x faster simulations, a new parallelization adaptor with %dofuture%, a refreshed API that aligns

Dear all, I am happy to announce that {simtrial} is now on CRAN (https://cran.r-project.org/package=simtrial). simtrial is a fast and extensible clinical trial simulation framework for time-to-event endpoints. This release brings a new tabular data processing engine powered by data.table for 3x to 5x faster simulations, a new parallelization adaptor with %dofuture%, a refreshed API that aligns

I wonder whether the functions for skewness and kurtosis in the e1071 package are based on correct formulas. The functions in the package e1071 are: # -------------------------------------------- skewness <- function (x, na.rm = FALSE) { if (na.rm) x <- x[!is.na(x)] sum((x - mean(x))^3)/(length(x) * sd(x)^3) } # -------------------------------------------- and #

Hi, I have a series of id's with multiple visits and questionnaire scores. This is a clinical trial that will be analyzed using the last observation carried forward method. In other words, in order to comply with intent to treat analysis when many subjects withdraw, data points for the last visit must be generated and filled in with the last observation. The ultimate goal is to tabulate the

Data from Fisher's paper: Confidence Limits for a Cross-Product Ratio. > y col1 col2 [1,] 10 3 [2,] 2 15 fisher.test(y) Fisher's Exact Test for Count Data data: y p-value = 0.0005367 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 2.753438 300.682787 sample estimates: odds ratio 21.30533 The crude odds