similar to: Log rank test power calculations

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

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

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?

Hi everyone, I was wondering whether extension of the current spower function for Hmisc were existing? My current focus is to calculate sample size based on the log-rank test with more than 2 groups (with/without trend) Taking into account the loss of follow up and the accrual processes. A SPSS library (ART) is existing but can't find anything in R Any information is welcome ! Many

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Hi folks, Using R Version 1.6.2 (2003-01-10) on SuSE Linux 7.2, I just installed Hmisc_1.5-3.tar.gz and Design_1.1-5.tar.gz These were taken from http://hesweb1.med.virginia.edu/biostat/s/library/r Checked the dependencies: Hmisc: grid, lattice, mva, acepack -- all already installed Design: Hmisc, survival -- survival already installed, so installed Hmisc first All seems to go

hie Im trying to calculate the power of the logrank test for different values of rho .I was just wandering whether the following programme would do it. any suggestions are welcome s=50 number=1 count1=0;count2=0;count3=0;count4=0;count5=0;count6=0;count7=0;count7=0; count8=0;count9=0 while(s!=0){ n=20 n1=n/2

useR's, I am trying to do a power calculation for a survival analysis using a logrank test and I need some help properly doing this in R. Here is the information that I know: - I have 2 groups, namely HG and LG - Retrospective analysis with subjects gathered from archival data over 20 years. No new recruitment of subjects and no estimated time to target accrual and accrual rate. - Survival

> On Feb 14, 2018, at 5:26 PM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: >> >> Hi all, >> >> The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. >>

Is there an R package available that would facilitate doing a power/sample size analysis for linear mixed effects models? I have seen the Java applets made available by Russell Length which would seem to be able to handle most any lme, but there is little documentation and it's not clear how the models need to be formulated. Rick B.

Greetings, I'm working on some analyses where I need to calculate wilcox tests for paired samples. In my current literature search I've found a few papers on sample size determination for the wilcox test notably: Sample Size Determination for Some Common Nonparametric Tests Gottfried E. Noether Journal of the American Statistical Association

Hi, keine ahnung. Das liegt jetzt bei Hr. Feld. Frag mal bei ihm nach. Gr??e Thushyanthan r-help-request at r-project.org wrote: > Send R-help mailing list submissions to > r-help at r-project.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://stat.ethz.ch/mailman/listinfo/r-help > or, via email, send a message with subject or body 'help'

Hello, I noticed that feeding 'clang -O3' with functions like: int sum1(int x) { int ret = 0; for(int i = 0; i < x; i++) ret += i; return ret; } int sum2(int x) { int ret = 0; for(int i = 0; i < x; i++) ret += i*i; return ret; } ... int sum20(int x) { int ret = 0; for(int i = 0; i < x; i++) ret +=

I have been attempting some basic power calculations using R and I am not getting the results I expect. I had a homework assignment in SAS, but I want to learn R as well, so I was attempting to reproduce my result. (No one else in the class is doing R, so there's no need to obsfucate the answer, the SAS code is what I get my grade for.) The code I am using is: # You assume that the

I sent a message a couple days ago about doing calculations for power of the ANOVA. Several people got back to me very quickly which I really appreciated. I'm working now on a similar problem, but instead of a balanced ANOVA, I have an unbalanced one. The first part of the question was: You assume that the within-population standard deviations all equal 9. You set the Type 1 error rate at á

> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: > > Hi all, > > The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. > > But according to several sources including "survminer" package

Power calculations two sample test for proportions is very useful. Is there a way however, to get away from the two samples being of the same size. What would happen if one had n=15 in the one sample and n=45 in the other sample. Farrel Buchinsky, MD Pediatric Otolaryngologist Allegheny General Hospital Pittsburgh, PA **********************************************************************

I'm having difficulties with plot.locfit.3d, at least I think that is the problem. I have a large dataframe (about 4 MM cases) and was hoping to see a non-parametric estimate of the hazard plotted against two variables: > fit <- locfit(~surv.yr+ ur_protein + ur_creatinine, data=TRdta, cens = 1-death, family = "hazard", xlim=c(0,10)) # it took somewhere between 1 and 2

My R_version is 2.11.1. I can?t install R packages. The error massages are following: package 'DBI' successfully unpacked and MD5 sums checked error in normalizePath(path) : path[1]="E:\R-2.11.1\library/DBI"?? -- View this message in context: http://r.789695.n4.nabble.com/A-question-of-install-R-packages-tp2327716p2327716.html Sent from the R help mailing list archive at

Hello, I'm working on a Cox Proportional Hazards model for a cancer data set that has missing values for the categorical variable "Grade" in less than 10% of the observations. I'm not a statistician, but based on my readings of Frank Harrell's book it seems to be a candidate for using multiple imputation technique(s). I understand the concepts behind imputation, but using