similar to: Non-inferiority log-rank test

Hello Everyone,   I'm trying to conduct a couple of power analyses and was hoping someone might be able to help. I want to estimate the sample size that would be necessary to adequately power a couple of non-inferiority tests. The first would be a log-rank test and the second would be a Wilcoxon rank-sum test. I want to be able to determine the sample size that would be necessary to test for a

It returns a chi-squared statistic with one degree of freedom. -- David Winsemius -------------- Original message ---------------------- From: Timthy Chang <henchao.chang at gmail.com> > > > >See the cpower() and spower() functions in Frank Harrell's Hmisc package > >on CRAN. > > > >HTH, > > > >Marc Schwartz > > How to calculate the

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 R-Users, Is anyone aware of a significance test which allows demonstrating that one distribution dominates another? Let F(t) and G(t) be two distribution functions, the alternative hypothesis would be something like: F(t) >= G(t), for all t null hypothesis: F(t) < G(t), for some t. Best wishes, Matthias PS. This one would be ok, as well: F(t) > G(t), for all t null

I would like to do some power estimations for a log-rank two sample test and cpower seems to fit the bill. I am getting confused though by the man page and what the arguments actually mean. I am also not sure whether cpower takes into account censoring or not. Could anyone provide a simple example of how I would get the power for a set control/non-control clinical trial where censoring occurs at

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

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

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:? >

Hi, We have an experiment with pass/fail outcome, and a continuous parameter which may contribute to the outcome. First, we've analyzed it by: p=c(F,T,F,F,F,T,T,T,T,T,T,T,F,T,T,T,T); w=c(53,67,59,59,53,89,72,56,65,63,62,58,59,72,61,68,63); l<-glm(p~w,family=binomial) summary(l) Which turned out to be non significant. Then, we thought of comparing the parameters of the two groups

> 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

I have seen several queries about parameterizing the negative binomial scale parameter. This is called the heterogeneous negative binomial. I have written a function called "nbinomial" which is in the msme package on CRAN. Type ?nbinomial to see the help file. The default model is a negative binomial for which the dispersion parameter is directly related to mu, which is how Stata,

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 spacing:?gsDesign(k=2,test.type=2,timing=c(0.75,1),alpha=0.05,sfu='Pocock')Symmetrictwo-sided group sequential

Try changing your method to "ML" and try again. I tried the run the first example from the documentation and it failed with the same error. Changing the estimation method to ML worked. @List: Can anyone else verify the error I got? I literally ran the following two lines interactively from the example for goodfit: dummy <- rnbinom(200, size = 1.5, prob = 0.8) gf <- goodfit(dummy,

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've tested it on MSVC, gcc, clang and icc. The solution in clang (in Decl.h) is not ideal (as you have said yourself). The solution I offer, is using a union of fields of class BitField (this is a new class that implements a bitfield of a specific type requested). With this, the definition, of the required bitfields in the subclass data, remains in the hands of the actual class needing them.

On Mon, Apr 27, 2020 at 6:42 AM Robinson, Paul via llvm-dev < llvm-dev at lists.llvm.org> wrote: > Older releases are still available for download at releases.llvm.org; I > believe the 3.0 release was supposed to be able to read 2.x bitcode, so you > should be able to upgrade the bitcode with 3.0 tools and proceed from > there. I **think** everything since 3.0 is still readable

Does anyone have any ideas how I could do a power calculation for a log rank test. I would like to know what the suggested sample sizes would be to pick a difference when the control to active are in a ratio of 80% to 20%. Thanks Dan -- ************************************************************** Daniel Brewer, Ph.D. Institute of Cancer Research Email: daniel.brewer at icr.ac.uk

Hello, Sample code: library("vcd") dummy <- rnbinom(200, size=1.5, prob=0.8) gf <- goodfit(dummy, type="nbinomial", method="MinChisq") plot(gf) I would like to: 1. add a lgened stating the bars show the actual counts and the red dots - the fit. 2. show the goodness-of-fit values calculated somewhere on an empty white space ob the plot. But... the legend

Hey, Does anyone know if there is a way to get back from goodfit what it estimated the parameters to be? I used the code fit<-goodfit(round(data$PLX_NRX),type="nbinomial" and got a pretty good fit. I could not however duplicate this good fit with any parameter estimates that I had. Any ideas??? Thanks, Elizabeth Lawson ---------------------------------