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...are not standard normal. My back up option is to use a Kolmogorov-Smirnov test, but my impression is that that is not a very powerful test. Any suggestions? Thanks, Alan Herschtal Senior Biostatistician Peter MacCallum Cancer Centre Phone +61 3 9656 3639 Fax +61 3 9656 1420 Email alan.herschtal@petermac.org This email (including any attachments or links) may contain confidential and/or legally privileged information and is intended only to be read or used by the addressee. If you are not the intended addressee, any use, distribution, disclosure or copying of this email is strictly prohibite...

...he differences eg col=gray(0.95). I cant seem to come up with a simple method for this. Any pointers in the right direction much appreciated. Cheers Scott _____________________________ Dr. Scott Williams MBBS BScMed FRANZCR Peter MacCallum Cancer Centre Melbourne, Australia scott.williams at petermac.org

...ction to do this without tedious and slow loops. Any help greatly appreciated. Cheers Scott _____________________________ Dr. Scott Williams MBBS BScMed FRANZCR Radiation Oncologist Peter MacCallum Cancer Centre Melbourne, Australia ph +61 3 9656 1111 fax +61 3 9656 1424 scott.williams at petermac.org

...using apply() or the like could be devised but I cant presently understand the necessary syntax. An suggestions appreciated. Thanks Scott _____________________________ ? Dr. Scott Williams Peter MacCallum Cancer Centre Melbourne, Australia ph +61 3 9656 1111 fax +61 3 9656 1424 scott.williams at petermac.org? ? This email (including any attachments or links) may contain confidential and/or legally privileged information and is intended only to be read or used by the addressee. If you are not the intended addressee, any use, distribution, disclosure or copying of this email is strictly prohi...

...need the coefficients if at all possible. I cant find anything in the help files suggesting that I cant use coef.reps in a cph model. Any help appreciated. Cheers Scott _____________________________ Dr. Scott Williams MD Peter MacCallum Cancer Centre Melbourne, Australia scott.williams at petermac.org

...group=group)) I am sure a simple solution is out there. Any help appreciated, Scott _____________________________ Dr. Scott Williams MBBS BScMed FRANZCR Radiation Oncologist Peter MacCallum Cancer Centre Melbourne, Australia ph +61 3 9656 1111 fax +61 3 9656 1424 scott.williams@petermac.org This email (including any attachments or links) may contain confidential and/or legally privileged information and is intended only to be read or used by the addressee. If you are not the intended addressee, any use, distribution, disclosure or copying of this email is strictly prohibite...

Hi all, I am aware that crr lacks the "friendly" command structure of functions such as cph. All is clear to me about including covariates until I want to include a stratification term in the competing risk framework (no nice strat command). I am still a bit of a novice in R - I am looking for an example to help me with this, but can't seem to find one. Any advice appreciated (no

I am trying to build a model to aid a clinical decision. Certain patients have a blood marker measured at each visit - a rise of this may indicate recurrence of the cancer after treatment (endpoint is "clinical recurrence", censored). In a proportion (up to 30%), this rise is a false positive - hence I wish to correlate factors at the time of the rising test to clinical recurrence,

Hi all, I am trying to produce a series of plots showing the prevalence of a condition, which is subject to censoring. In most cases the condition is temporary and resolves with time. I would like to use the method of Pepe et al Stat Med 1991; 413-421 - essentially the prevalence is the Kaplan-Meier prob[having the condition at time t] - KM prob[recovery by time t] (also divided by 1-KM[death by

Hi R experts, I am trying to do a prognostic model validation study, using cancer survival data. There are 2 data sets - 1500 cases used to develop a nomogram, and another of 800 cases used as an independent validation cohort. I have validated the nomogram in the original data (easy with the Design tools), and then want to show that it also has good results with the independent data using 60

A simple question - using the following fishers test it appears that the P value is significant, but the CI includes 1. Is this result correct? > data.50p10min <- matrix(c(16,15, 8, 24),nrow=2) > fisher.test(data.50p10min) Fisher's Exact Test for Count Data data: data.50p10min p-value = 0.03941 alternative hypothesis: true odds ratio is not equal to 1 95