similar to: gsDesign Pocock & OBF boundary

> On 23 Sep 2017, at 01:32, array chip via R-help <r-help at r-project.org> wrote: > > Hi, > > I am learning to use your gsDesign package! I have a question about Pocock and OBF boundary. As far as Iunderstand, these 2 boundaries require equal spacing between interim analyses(maybe this is not correct?). But I can still use gsDesign to run an analysisbased on unequal

Sorry for messed up text. Here it goes again: I am learning to use the gsDesign package. I have a question about Pocock and OBF boundary. As far as I can understand, these 2 boundaries require equal spacing between interim analyses (maybe this is not correct?). But looks like I can still use gsDesign to run an analysis based on unequal spacing:? >

Still failed. The first secret is in your email program settings, to use Plain Text format (at least for emails you send to this mailing list). The second secret tool to use is the reprex package to let you verify that your code example will do on our computers what it is doing on your computer before you send it to us. That will also involve giving us some sample data or referencing some data

I'm trying to use gsDesign for a noninferiority trial with binary endpoint. Did anyone know how to specify the trial with different sample sizes for two treatment groups? Thanks in advance! [[alternative HTML version deleted]]

Dear all, I'm excited to announce that a new version of gsDesign (3.6.0) is now on CRAN (https://cran.r-project.org/package=gsDesign). gsDesign supports group sequential clinical trial design, largely as presented in Jennison and Turnbull (2000). The 3.6.0 update introduces some significant new features and enhancements: - New gsSurvCalendar() function to enable group sequential design for

Dear all, I'm excited to announce that a new version of gsDesign (3.6.0) is now on CRAN (https://cran.r-project.org/package=gsDesign). gsDesign supports group sequential clinical trial design, largely as presented in Jennison and Turnbull (2000). The 3.6.0 update introduces some significant new features and enhancements: - New gsSurvCalendar() function to enable group sequential design for

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

Hello everyone, I need to do a sample size calculation. The study two arms and two endpoints. The two arms are two different cancer drugs and the two endpoints reflect efficacy (based on progression free survival) and toxicity. Until now, I have been trying to understand this in terms of a one-arm design, where the acceptable rate of efficacy might be 0.40, the unacceptable rate of efficacy

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Hi all, How can I overcome the error "object of type 'closure' not subsettable" I ran the following script seq <- paste(seq(1914, 1916, by=1), "*.y", sep=".") # make sequence c <- 3 # total number of files d2 <- file # creates dummy file # Input sequence in loop for (i in 1:3){ list <- list.files("~/ukcp09/txt/x.djf", seq[[i]]) file

#include <stdio.h> #include "vorbis/codec.h" #include "vorbis/vorbisfile.h" #include <io.h> #include <fcntl.h> #pragma comment(lib, "vorbisfile_d") int main(int argc, char **argv) { FILE *fp; OggVorbis_File obf; if(argc < 1) return -1; if((fp = fopen("f:\\testogg.ogg", "rb")) == NULL) { printf("Open Files

Dear All,   is there a function in R that would help me convert a covariance matrix built based on arithmetic returns to a covariance matrix from log-returns?   As an example of the means and covariance from arithmetic:   mu <-c(0.094,0.006,1.337,1.046,0.263) sigma

Hi Group, I'm looking for a method to calculate sample size for a non-inferiority survival analysis. Initially I've used cpower() of Hmisc, switching the roles of type-I and type-II errors, as cpower() assumes no treatment difference as the null hypothesis. Now, I would like to introduce event rate difference under the null-hypothesis, and calculate sample size accordingly. Any ideas?

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Hi all; I want to print system.time whenever I execute any command. It takes too much time to type "system.time()" function to all command. is there any solution on it? And, apply(matrix,1,cumsum) command is too slow to some large matrix. is there any function like rowCumSums ? thank u! -- View this message in context:

Folks, there is an issue pretty buried in the commits list that I suspect should have wider visibility. See r262188 and subsequent discussion. To summarize: it appears that mingw32 was unable to correctly produce a static data member when instantiated as a base class. The "fix" is to then explicitly instantiate the base class separately from its use in a base class. I think this is

can someone show me how to convert a table to a data.frame or a matrix ? I tried below and as.data.frame rearranges the columns similarly to a melt from reshape and as.matrix didn't change it. I actually would prefer to change it to a dataframe but if someone can show me how to convert it to a matrix, then as.data.frame will work on that. I was thinking about just changing the class to

On Tue, Mar 1, 2016 at 4:21 PM Reid Kleckner via llvm-dev < llvm-dev at lists.llvm.org> wrote: > First, I'd like to say it would be great if we could get away from relying > on these globally unique pass IDs represented as addresses of globals. Long > ago I tried to hack up a DLL build of LLVM and quickly discovered that > these IDs are the biggest source of dllimported

hello for exampl, i have this programme # Generating data which are right truncated library(DTDA) library(splines) library(survival) n<-25 X<-runif(n,0,1) V<-runif(n,0.75,1) for (i in 1:n){ while (X[i]>V[i]){ X[i]<-runif(1,0,1) V[i]<-runif(1,0.75,1) }} res<-lynden(X=X,U=NA, V=V, boot=TRUE) attach(res) temps = time M_i = n.event L_t = res

I have a simple table below called temptable and i want to obtain the same structure that prop.table creates except get the counts rather than the proportions. margin.table seems to create one table with columns and rows whereas I am looking for the three table type structure that prop.table gives. Thanks. temptable<-structure(c(0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,