From: Wittner, Ben, Ph.D. <Wittner.Ben_at_mgh.harvard.edu>

Date: Thu, 31 Jan 2008 11:39:25 -0500

n.2 <- m/((k*pf$p.E) + pf$p.C)

list(n.1=n.1, n.2=n.2)

}

list(p.C=sum(D), p.E=sum(E))

}

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 31 Jan 2008 - 16:42:27 GMT

Date: Thu, 31 Jan 2008 11:39:25 -0500

I suspect that someone will write saying that what you want is in one or a few packages, but you might also find the code below useful. (Note, the first two functions require the third.) -Ben

*##
**## Do power calculation of Freedman for Cox PH as set forth on page 733
**## of 5th edition of Rosner, "Fundamentals of Biostatistics"
**##
**## k = ratio of (# subjects in exposed group) to
**## (# subjects in control)
**## t = max time of follow-up
**## RR = (hazard rate for exposed group) /
**## (hazard rate for control group)
**## lambda[j] = Rosner's lambda_(j-1)
*

## delta[j] = Rosner's delta_(j-1)

*##
*

survivalUtil.power <- function(n.1, n.2, t, RR, lambda, delta, alpha=0.05) {
pf <- survivalUtil.probFail(t, RR, lambda, delta)
k <- n.1/n.2

m <- n.1*pf$p.E + n.2*pf$p.C

pnorm(sqrt(k*m)*abs(RR - 1)/(k*RR + 1) - qnorm(1 - alpha/2))
}

*##
**## Do sample-size calculation of Freedman for Cox PH as set forth
**## on page 735
**## of 5th edition of Rosner, "Fundamentals of Biostatistics"
**##
**## k = ratio of (# subjects in exposed group) to
**## (# subjects in control)
**## t = max time of follow-up
**## RR = (hazard rate for exposed group) /
**## (hazard rate for control group)
**## lambda[j] = Rosner's lambda_(j-1)
**## delta[j] = Rosner's delta_(j-1)
**##
**## Value: n1 - number of people needed in exposed group
*

## n2 - number of people needed in control group

*##
*

survivalUtil.sampSize <- function(k, t, RR, lambda, delta,

alpha=0.05, beta=.2) { m <- ((1/k)*(((k*RR + 1)/(RR - 1))^2)* ((qnorm(1 - (alpha/2)) + qnorm(1 - beta))^2))pf <- survivalUtil.probFail(t, RR, lambda, delta) n.1 <- (m*k)/((k*pf$p.E) + pf$p.C)

n.2 <- m/((k*pf$p.E) + pf$p.C)

list(n.1=n.1, n.2=n.2)

}

*##
**## Compute probabilties of failure used by survivalUtil.power() and
*

## survivalUtil.sampSize()

*##
*

survivalUtil.probFail <- function(t, RR, lambda, delta) {

A <- cumprod(1 - lambda)[1:t] B <- cumprod(1 - (RR*lambda))[1:t] C <- cumprod(1 - delta)[1:t] D <- lambda[2:(t + 1)]*A*C E <- RR*lambda[2:(t + 1)]*B*C

list(p.C=sum(D), p.E=sum(E))

}

> -----Original Message----- > From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] > On Behalf Of Daniel Brewer > Sent: Thursday, January 31, 2008 10:53 AM > To: r-help_at_stat.math.ethz.ch > Subject: [R] 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 > ************************************************************** > > > The Institute of Cancer Research: Royal Cancer Hospital, a charitable > Company Limited by Guarantee, Registered in England under Company No. > 534147 with its Registered Office at 123 Old Brompton Road, London SW7 > 3RP. > > This e-mail message is confidential and for use by the a...{{dropped:2}} > > ______________________________________________ > R-help_at_r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code.

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